Code Reviewer for LLM Data Training (SQL) Job at G2i Inc.

Sr. SWE for Code Reviewing LLM Data Training (SQL)

We are looking for a Code Reviewer with expertise in SQL to join our AI training...

Location

Salary:

Not provided

G2i Inc.

Expiration Date

Until further notice

Requirements

5–7+ years of experience in SQL development, QA, or code review
Strong understanding of core SQL concepts and development best practices
Experience with debugging, testing tools, and code execution environments
Strong analytical and critical thinking skills
Excellent written communication for documenting evaluations and feedback
English proficiency at B2, C1, C2, or Native level

Job Responsibility

Audit annotator evaluations of AI-generated SQL responses
Verify if responses follow prompt requirements regarding query logic, joins, and correct SQL syntax
Evaluate SQL code for correctness, performance, security, and readability
Run and validate proof-of-work code submitted by annotators to confirm functionality
Ensure responses align with instruction-following expectations including style, tone, and clarity
Identify and document errors, omissions, or rating inconsistencies
Provide clear, constructive QA feedback based on rubric and guideline criteria
Collaborate with internal teams for clarifications on complex or ambiguous items

Senior Databricks ML & AI Engineer

Location

Salary:

Not provided

Myticas Consulting

Expiration Date

Until further notice

Requirements

5+ years of hands-on Machine Learning Engineering, MLOps, or AI Engineering experience within enterprise production environments
Extensive experience designing and deploying Databricks Lakehouse solutions using Delta Lake, Unity Catalog, MLflow, Databricks SQL, Workflows, and Delta Live Tables
Strong programming expertise in Python, PySpark, Pandas, NumPy, and modern software engineering best practices
Experience building, training, deploying, and monitoring machine learning models using PyTorch, TensorFlow, scikit-learn, XGBoost, or similar ML frameworks
Proven experience implementing end-to-end MLOps pipelines including experiment tracking, model registry, automated retraining, model deployment, and production monitoring
Hands-on experience developing Generative AI (GenAI) and Large Language Model (LLM) solutions, including RAG architectures, prompt engineering, LangChain, LlamaIndex, or Databricks Mosaic AI
Experience implementing Vector Search, embeddings, semantic search, and AI retrieval pipelines using Databricks or similar vector database technologies
Strong understanding of Apache Spark internals, distributed computing, performance tuning, partitioning, memory management, and large-scale data processing
Experience with cloud platforms including AWS, Azure, or Google Cloud Platform, supporting enterprise AI and machine learning workloads
Experience implementing CI/CD pipelines, GitHub Actions, Azure DevOps, Databricks Asset Bundles, and modern DevOps automation practices

New

Principal Machine Learning Engineer - Forecasting

We are seeking a Principal Machine Learning Engineer to join the Forecasting tea...

Location

India , Hyderabad

Salary:

Not provided

Amgen

Expiration Date

Until further notice

Requirements

Degree and 12+ years of experience in machine learning engineering, software engineering, data science engineering, or a related quantitative discipline.
10+ years of professional experience building, deploying, and operating production ML, AI, data, or software systems, including significant experience as a technical lead on complex, cross-functional initiatives.
Demonstrated track record of designing or architecting new and existing systems with emphasis on reliability, scale, security, maintainability, and operational excellence.
Deep hands-on experience with the full ML engineering lifecycle, including data pipelines, feature engineering, experimentation, model training, model integration, testing, deployment, monitoring, evaluation, observability, and continuous improvement.
Strong experience deploying forecasting, probabilistic, Bayesian, predictive, NLP, deep learning, or LLM-based systems in production environments.
Experience building or integrating AI systems, including LLM-powered applications, agentic workflows, retrieval or information-retrieval systems, evaluation frameworks, and human-in-the-loop review patterns.
Strong object-oriented programming skills in Python and SQL, with experience using modern ML and software development frameworks such as scikit-learn, PyTorch, TensorFlow/JAX, Spark, Ray, MLflow, Airflow/Prefect/Dagster, FastAPI, or equivalent technologies.
Experience with cloud platforms and distributed systems, including containerization, CI/CD, infrastructure-as-code, model serving, workflow orchestration, batch and streaming data processing, and production support.
Strong software engineering fundamentals, including system design, architecture trade-off analysis, testing strategies, code reviews, source control, build and release processes, performance optimization, and maintainability.
Demonstrated ability to communicate technical strategy, system tradeoffs, and delivery risks to technical and non-technical stakeholders, including senior leaders, product/program owners, scientists, and business partners.

Job Responsibility

Define and drive the technical strategy for enterprise forecasting and AI decision systems, aligning architecture, reusable platforms, and delivery roadmaps to Amgen's planning, supply, commercial, manufacturing, operations, and patient-focused priorities.
Partner with data scientists, product and program leaders, operations, commercial, manufacturing, supply chain, finance, and other business stakeholders to translate ambiguous requirements into shipped software and measurable business outcomes.
Architect, build, and scale production ML, LLM, and agentic AI systems that combine forecasting, predictive analytics, simulation, optimization, and autonomous or semi-autonomous workflow automation.
Productionize advanced statistical, Bayesian, deep learning, and machine learning models, including training, validation, inference, serving, evaluation, lifecycle management, and governed deployment.
Lead development of AI agent components that automate complex forecasting and operational workflows across multiple systems, decision points, datasets, and user groups while preserving appropriate human-in-the-loop review and escalation patterns.
Design secure integrations across enterprise APIs, databases, analytics platforms, workflow systems, cloud services, and AI orchestration patterns to enable multi-system decision support and scalable automation.
Establish robust MLOps and AI engineering capabilities, including model versioning, CI/CD, automated retraining, performance monitoring, observability, drift detection, service-level reliability, rollback strategies, and operational runbooks.
Implement guardrails, model and agent evaluation frameworks, auditability, explainability, responsible AI controls, and human-in-the-loop operating models for production AI systems in high-impact and regulated business contexts.
Research and evaluate state-of-the-art open-source, vendor, and internal tools related to forecasting, LLMs, AI agents, MLOps, model optimization, model serving, and scalable AI infrastructure for potential application to Amgen business problems.
Provide principal-level technical mentorship, design review leadership, and engineering standard-setting across teams, promoting code quality, documentation, reproducibility, testing, security, privacy, maintainability, and operational excellence.

Fulltime

Senior Ml Engineer

Location

Colombia , Medellín, Antioquia;Bogotá, Capital District;Cali, Valle del Cauca;Barranquilla;Bucaramanga, Santander

Salary:

Not provided

Provectus

Expiration Date

Until further notice

Requirements

ML Fundamentals: supervised, unsupervised, and reinforcement learning
Model Development: feature engineering, model training, evaluation, hyperparameter tuning, and validation
ML Frameworks: classical ML libraries, TensorFlow, PyTorch, or similar frameworks
Deep Learning: CNNs, RNNs, Transformers
LLM Applications: Experience building production LLM-based applications
Prompt Engineering: Ability to design effective prompts and chain-of-thought strategies
RAG Systems: Experience building retrieval-augmented generation architectures
Vector Databases: Familiarity with embedding models and vector search
LLM Evaluation: Experience with evaluation metrics and techniques for LLM outputs
Python: Advanced proficiency in Python for ML applications

Job Responsibility

Design and implement end-to-end ML solutions from experimentation to production
Build scalable ML pipelines and infrastructure
Optimize model performance, efficiency, and reliability
Write clean, maintainable, production-quality code
Conduct rigorous experimentation and model evaluation
Troubleshoot and resolve complex technical challenges
Mentor junior and mid-level ML engineers
Conduct code reviews and provide constructive feedback
Share knowledge through documentation, presentations, and workshops
Collaborate with cross-functional teams (DevOps, Data Engineering, SAs)

What we offer

Long-term B2B collaboration
Fully remote setup
A budget for your medical insurance
Paid sick leave, vacation, public holidays
Continuous learning support, including unlimited AWS certification sponsorship

Fulltime

Senior Machine Learning Engineer, Model Risk Management

Block lends, moves money, and screens for financial crime at enormous scale, and...

Location

United States , New York

Salary:

194500.00 - 343100.00 USD / Year

Cash App

Expiration Date

Until further notice

Requirements

A quantitative degree or equivalent experience, and senior-IC depth building or validating models in a high-stakes domain such as credit, fraud, or financial crime
Command of effective-challenge methodology: reproduction, conceptual-soundness review, benchmarking, stress testing, and outcomes analysis, with an eye for how a model holds up after launch and where its assumptions break
Deep applied ML and statistics across model families, from regression and tree ensembles to deep learning, with sound judgment about evaluation, calibration, and generalization
Experimentation and statistical rigor: holdout and experiment design, reasoning about uncertainty, and evaluating a model beyond aggregate accuracy
Solid software and data engineering: production-quality Python, SQL on large datasets, and reproducible, tested code
Fluency with modern AI: building with LLMs and agentic tools, and the judgment to know when their output can be trusted
Familiarity with model risk management frameworks and fair-lending standards, with the specifics learnable on the job
The communication to explain and defend your conclusions to model owners and senior stakeholders, and the independence to operate under ambiguity

Job Responsibility

Independently challenge model owners across lending, fraud, and AML: reproduce their results, set and defend the acceptance thresholds, and own the call on whether a model is sound
Hunt the silent errors that make metrics lie, and prove them out before they reach production
Choose evaluation that holds up under real conditions: rare events, shifting populations, and drift that only shows up after launch
Work hands-on in codebases you did not write, learning the data, configs, and conventions, and ship production code in the tooling you build to validate them
Build the agentic validation tooling the team depends on, orchestrating agents that run in parallel
Reason about ML systems end to end — how features, training, serving, monitoring, and scale fit together — to evaluate and challenge an owner's design
Tie explainability and fair-lending findings on consumer credit models back to the model and product decisions that follow
Help define how Block validates the systems at the frontier of production AI, setting standards where none exist yet

What we offer

Remote work
Medical insurance
Flexible time off
Retirement savings plans
Modern family planning

Fulltime

Data Engineer III

Robert Half, one of FORTUNE’s World’s Most Admired Companies and a Fortune 100 B...

Location

United States , San Ramon

Salary:

Not provided

Robert Half

Expiration Date

Until further notice

Requirements

Bachelor's in Computer Science, Engineering, or related field (Master's preferred)
5+ years with Python and SQL in data engineering for big data ML/analytics workloads
5+ years designing, building, and troubleshooting scalable ETL/ELT pipelines for business-critical production systems
3+ years with cloud data services (AWS), container orchestration (Docker, Kubernetes), and IaC (Terraform, CloudFormation)
3+ years architecting ML workflows and data platforms with CI/CD, automated testing, and distributed processing (Spark)
3+ years collaborating cross-functionally with Data Science, MLOps, Platform Engineering, and DevOps teams
3+ years implementing data quality testing and optimizing SQL/Python for cost/performance in the cloud
Understanding of the full Data Science SDLC, and experience mentoring engineers
Strongly Preferred - 2+ years hands-on with Databricks (Delta Lake, Unity Catalog, Databricks SQL)
Experience with MLflow experiment tracking and model registry workflows

Job Responsibility

Lead architecture and design of complex data pipelines on Databricks lakehouse architecture (Unity Catalog, Delta Lake, Structured Streaming)
Define technical approach for data engineering initiatives, mentor less-senior engineers, and set standards for code quality through leadership and code reviews
Design and build data foundations that enable AI/ML capabilities — feature stores, embedding pipelines, vector search indexes, and model training datasets
Align data engineering solutions with business strategy, including support for Agentic AI workloads
Own health, scalability, and modernization of data infrastructure with Databricks as the strategic platform — including workload migration, compute optimization, and Unity Catalog adoption
Optimize pipeline performance (Delta Lake table layouts, clustering, Z-ordering) and establish monitoring/alerting best practices with clear SLAs
Build data infrastructure supporting Agentic AI systems — real-time data access layers, context retrieval pipelines, and agent-accessible data services
Collaborate cross-functionally with DevOps, Platform Engineering, and MLOps roles to integrate data solutions into the broader technology environment and shared AI infrastructure – Mlflow registries, feature stores, and agent orchestration layers
Provide consultation to Senior Leadership on complex projects and drive continuous improvement initiatives
Champion data governance at all layers for data, models, and AI assets

What we offer

medical
vision
dental
life and disability insurance
401(k) plan
free online training

Fulltime

Ai Engineer - Azure & C# .Net

We are seeking a capable and solutions‑focused AI Engineer to join our growing A...

Location

United Kingdom

Salary:

60000.00 GBP / Year

360 Resourcing Solutions

Expiration Date

Until further notice

Requirements

Hands-on experience building solutions with Azure AI Services and integrating them into applications and/or data solutions
Working knowledge of Azure OpenAI and common GenAI patterns (prompting, evaluation, basic RAG)
Some experience with ML delivery (training, packaging, deployment, monitoring) in a live environment
Proficiency in C#/.NET plus SQL fundamentals
Understanding of vector search concepts (embeddings, chunking, retrieval) and secure API integration
Experience using Git-based workflows and CI/CD pipelines (e.g., GitHub or Azure DevOps)
Strong communication and problem-solving skills, including clear technical documentation

Job Responsibility

Build and enhance GenAI solutions using Azure OpenAI, Azure AI Services, and Copilot extensibility, following agreed patterns
Implement RAG architectures (vector retrieval, embeddings, prompt strategies) with secure LLM integrations
Build conversational assistants and workflow automations using Copilot Studio and the Power Platform
Contribute to experimentation, prototyping, and PoC development to evaluate AI capabilities and feasibility
Support evaluation and integration of third‑party AI tools or APIs where required, working with the team to meet governance and security requirements
Develop and operationalise ML models with appropriate review and documentation
Translate prototypes into robust services, collaborating with engineering colleagues to meet non-functional requirements
Create ML pipelines and automate lifecycle workflows using team tooling and standards
Assist with monitoring, optimisation, escalating risks and issues where appropriate
Build AI-enabled services and APIs using .NET/C#, Azure Functions, and REST under guidance on patterns and quality

Fulltime

Ai Engineer - Azure & C# .Net Or Python

We are seeking a capable and solutions-focused AI Engineer to join our growing A...

Location

United Kingdom

Salary:

60000.00 GBP / Year

360 Resourcing Solutions

Expiration Date

Until further notice

Requirements

Hands-on experience building solutions with Azure AI Services and integrating them into applications and/or data solutions
Working knowledge of Azure OpenAI and common GenAI patterns (prompting, evaluation, basic RAG)
Some experience with ML delivery (training, packaging, deployment, monitoring) in a live environment
Proficiency in either C#/.NET or Python, plus SQL fundamentals
Understanding of vector search concepts (embeddings, chunking, retrieval) and secure API integration
Experience using Git-based workflows and CI/CD pipelines (e.g., GitHub or Azure DevOps)
Strong communication and problem-solving skills, including clear technical documentation
Based in the UK with valid right to work

Job Responsibility

Build and enhance GenAI solutions using Azure OpenAI, Azure AI Services, and Copilot extensibility
Implement RAG architectures (vector retrieval, embeddings, prompt strategies) with secure LLM integrations
Build conversational assistants and workflow automations using Copilot Studio and the Power Platform
Contribute to experimentation, prototyping, and PoC development to evaluate AI capabilities and feasibility
Support evaluation and integration of third-party AI tools or APIs
Develop and operationalise ML models with appropriate review and documentation
Translate prototypes into robust services, collaborating with engineering colleagues
Create ML pipelines and automate lifecycle workflows using team tooling and standards
Assist with monitoring, optimisation, escalating risks and issues
Build AI-enabled services and APIs using .NET/C#, Python, Azure Functions, and REST

Fulltime

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Code Reviewer for LLM Data Training (SQL)

Job Description

Job Responsibility

Requirements

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