ML Engineer / MLOps Engineer Job at InfoGrowth (Chennai)

Sr Staff ML Engineer - Production & MLOps Focus - GenAI Security Platform

Join our team building a cutting-edge multi-tenanted GenAI Security Platform tha...

Location

India , Bengaluru

Salary:

Not provided

Palo Alto Networks

Expiration Date

Until further notice

Requirements

4+ years of ML engineering experience with hands-on LLM/NLP work
Practical experience building LLM-based applications (agents, multi-turn systems, evaluators)
Understanding of model fine-tuning, embedding optimization, and prompt engineering
Experience with LLM APIs (OpenAI, Anthropic, AWS Bedrock, Azure OpenAI)
Knowledge of LLM orchestration frameworks ( LangChain, LlamaIndex, Pydantic AI, custom solutions)
Familiarity with model architectures and when to fine-tune vs prompt engineer
Strong experience deploying ML models to production at scale
Experience with Model serving frameworks (vLLM preferred
TensorRT-LLM, Ray Serve, or similar a plus)
Kubernetes and Docker proficiency for ML workload orchestration

Job Responsibility

Build and deploy LLM-based agents and multi-step evaluation workflows
Fine-tune models, optimize embeddings, and manage model weights and artifacts
Deploy and scale ML services on Kubernetes with proper monitoring and resource management
Implement experiment tracking, model versioning, and deployment automation
Develop observability dashboards for ML metrics, costs, latency, and quality
Optimize LLM API usage through caching, batching, and intelligent routing strategies
Manage vector database infrastructure and semantic search systems
Create CI/CD pipelines for ML artifacts and automated testing frameworks
Collaborate with ML researchers to productionize prototypes and scale experiments

Fulltime

Senior ML Operations (MLOps) Engineer

Join our team as a Sr MLOps Engineer to help us bring current and next generatio...

Location

Salary:

Not provided

Eight Sleep

Expiration Date

Until further notice

Requirements

5+ years of software engineering experience with a focus on ML infrastructure, distributed systems, or large-scale data processing in Python (e.g., PyTorch, TensorFlow, or similar)
Hands-on experience with ML workflow orchestration and CI/CD pipelines for model deployment
Demonstrated success shipping ML models to production at scale, handling telemetry, monitoring, and feedback loops across large device fleets or user populations
Strong experience with AWS (Lambda, ECS, DynamoDB, CloudWatch) or equivalent cloud platforms for serving and monitoring ML systems
A fast-paced, collaborative, and iterative approach to tackling complex problems

Job Responsibility

Pioneer Cutting-Edge Technology: Introduce and implement cutting-edge ML technologies, integrating them into our products and processes to enable the future of health monitoring
End-to-End Ownership: Own design and operation of robust ML infrastructure – building scalable data, model, and deployment pipelines that ensure reliable delivery of models to production
Cross-functional Collaboration Partner with R&D, firmware, data, and backend teams to ensure ML inference operates reliably and scales to Pods everywhere
Optimize for Performance: Drive cost-effective, scalable, and high-performance ML systems by optimizing compute, storage, and deployment resources across training and inference
Enhance Tooling and Platforms: Develop tooling, micro services, and frameworks to streamline data processing, experimentation, and deployment
Effective Remote Communication: Thrive in a remote work environment, ensuring clear and direct communication

What we offer

Equity participation
Periodic equity refreshments based on performance
Your own Pod
Full access to health, vision, and dental insurance for you and your dependents
Supplemental life insurance
Flexible PTO
Commuter benefits to ease your daily commute
Paid parental leave

Fulltime

Senior Engineer / Lead Engineer - Virtual Engineering - AI ML

Sponsorship:  GM DOES NOT PROVIDE IMMIGRATION-RELATED SPONSORSHIP FOR THIS ROLE....

Location

India , Bengaluru

Salary:

Not provided

General Motors

Expiration Date

Until further notice

Requirements

Bachelor's or Masters Degree Mechanical/Automobile/Production /Mechatronics Engineering discipline or similar
5+ years in Automotive Manufacturing / Manufacturing Engineering Experience
1+ year experience in implementing AI/ML solutions in Automotive use cases
Should have executed at least 2 end-to-end projects in the text or Image data domain (from problem definition to deployment)
Strong programming skills in Python
Proficiency with ML/DL frameworks like Scikit-learn, TensorFlow, PyTorch, XGBoost
Solid understanding of statistics, probability, and linear algebra
Experience in data preprocessing, feature engineering, ETL and Exploratory Data Analysis (EDA)
Experience with MLOps platforms (MLflow, Kubeflow, Vertex AI, Azure ML)
Knowledge of ML model evaluation

Job Responsibility

Collaborate with stakeholders to understand business problems in the in the Manufacturing Engineering and Operations space and solve them using ML methodologies
Design, develop, and fine-tune AI/ML models for classification, regression, clustering, and recommendation systems
Work with MLOps tools to automate workflows, CI/CD pipelines, and model monitoring
Evaluate, validate, and benchmark model performance using appropriate metrics
Deploy AI models into production environments in collaboration with IT/AI teams
Establish monitoring and maintenance processes to ensure model accuracy over time
Ensure that all AI solutions comply with organizational data security, confidentiality, and regulatory requirements
Document workflows, results, and lessons learned for organizational knowledge sharing
Stay updated on advancements in ML model evaluation, ML frameworks, end-to-end ML pipelines

Fulltime

Mlops Engineer

NTT DATA strives to hire exceptional, innovative and passionate individuals who ...

Location

United States , Plano

Salary:

Not provided

NTT DATA

Expiration Date

Until further notice

Requirements

8+ Years experience with: MLOps Azure Kubernetes Argo / Bento Azure ML Studio Model Inferencing

Job Responsibility

Exercise expertise in ideating and developing ML applications on prediction, recommendation, text analytics, computer vision, bots, and document intelligence
Demonstrate deep knowledge of ML frameworks such as TensorFlow, PyTorch, Keras, Spacy, and scikit-learn
Employ technical knowledge and hands-on experience with Azure ML Studio and Azure Kubernetes Service
Experience in deploying Azure cloud services using Terraform templates with strong knowledge of DevOps principles and automated deployments
Leverage advanced knowledge of Python open-source software stack such as Django or Flask, Django Rest or FastAPI, etc.
Work on model inferencing, validation and deployments to ensure models are deployed with the appropriate levels of validation and quality
Create and maintain infrastructure to ingest, normalize, and combine datasets for actionable insights
Interact at appropriate levels to ensure client satisfaction and project success
Communicate complex technical concepts clearly to non-technical audiences

Fulltime

Machine Learning Engineer (MLOps Lead)

Role Highlights: Position: Machine Learning Engineer (MLOps Lead) Location: Lond...

Location

United Kingdom , London

Salary:

Not provided

RED Commerce - The Global SAP Solutions Provider

Expiration Date

Until further notice

Requirements

Strong MLOps / ML Engineering experience (5+ years)
Hands-on experience with GCP (Vertex AI)
Excellent Python development skills
Experience with CI/CD, Docker, Terraform
Ability to take ML models from research to production
Leadership or mentoring experience

Ai / Ml Engineer

Nextera Robotics is a fast-growing Physical AI company founded at MIT. We build ...

Location

United States , Boston

Salary:

130000.00 - 180000.00 USD / Year

Helpcare AI

Expiration Date

Until further notice

Requirements

Proven academic or industry experience building computer vision systems, including areas such as object detection, segmentation, visual understanding, or multimodal models
Strong Python skills, with the ability to build, test, and iterate quickly in a production-oriented environment
Proficiency using modern AI-assisted development tools such as coding agents, GitHub Copilot, or similar systems to accelerate implementation, debugging, and iteration
Solid problem-solving skills, strong ownership, and the ability to communicate clearly across technical and non-technical teams
Familiarity with data pipelines, model evaluation, and real-world ML workflows

Job Responsibility

Build and productionize advanced computer vision and multimodal AI systems for real-world industrial deployment - spanning detection, segmentation, visual understanding, and VLM/LLM-powered reasoning
Turn large-scale visual data collected by autonomous robots into actionable insights for progress tracking, safety monitoring, and project intelligence
Develop AI systems deployed across robotics, construction, energy, and telecommunications
Work closely with engineers and annotators to expand and improve high-quality training datasets
Support and scale machine learning operations (MLOps)
Build and maintain the cloud infrastructure and ML pipelines that power training, evaluation, and deployment

What we offer

Competitive salary based on experience, plus great perks
Join a YC-backed startup shaping the future of robotics

Fulltime

Senior ML Engineer

We are the global test and automation specialists, powering next-generation tech...

Location

United States , North Reading

Salary:

158600.00 - 253700.00 USD / Year

Teradyne

Expiration Date

Until further notice

Requirements

5+ years of experience in machine learning, applied AI, or related fields
Hands-on experience fine-tuning large language models
Experience with reinforcement learning (e.g., policy gradients, PPO, actor-critic methods)
Experience designing reward models or evaluation systems
Strong software engineering skills (Python, distributed systems familiarity)
Experience building production ML systems (MLOps, monitoring, deployment)
Ability to work cross-functionally with product, software, and hardware teams
Strong communication skills
comfortable engaging directly with customers & stakeholders
Computer vision skills in manufacturing inspection: defect detection, etc.

Job Responsibility

Own the technical direction and quality of all ML model development across Teradyne's AI initiatives, setting and enforcing engineering standards across the team
Lead end-to-end development of production ML systems: data ingestion and feature engineering, model architecture design, training pipelines, evaluation frameworks, and deployment
Design and implement novel ML approaches tailored to Teradyne's unique data domain including time-series parametric test data (STDF/TEMS), wafer map analysis, etc.
Drive applied research and model innovation, explore and evaluate new architectures, algorithms, and training methodologies, and translate promising approaches into production systems
Develop and maintain rigorous model evaluation frameworks, including validation methodologies, risk quantification, and production monitoring strategies
Lead technical design reviews
serve as final arbiter of ML architecture and modeling decisions for the team
Build and maintain production ML systems with a strong focus on reliability, scalability, and performance in Teradyne's ATE and manufacturing environments
Partner directly with customers and application engineers to understand real-world debug workflows and translate them into ML solutions
Mentor and develop junior ML engineers

What we offer

medical
dental
vision
Flexible Spending Accounts
retirement savings plans
life and disability insurance
paid vacation & holidays
tuition assistance programs

Fulltime

Senior ML Engineer (GenAI, AWS)

Location

Colombia , Medellín; Bogotá; Cali; Barranquilla; Bucaramanga

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

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ML Engineer / MLOps Engineer

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