GenAI / Machine Learning Engineer Job at Vodafone (Pune)

GenAI / Machine Learning Engineer - VOIS

We are seeking a GenAI / ML Engineer with 3-4 years of overall professional expe...

Location

India , Pune

Salary:

Not provided

Vodafone

Expiration Date

Until further notice

Requirements

3-4 years of overall experience in software engineering, AI/ML engineering, data engineering, cloud engineering, or related technology roles
At least 1 year of relevant domain experience in areas such as Generative AI, LLM-based applications, NLP or conversational AI, data or analytics engineering, cloud-native development, or enterprise chatbot platforms
Strong hands-on experience with Python for application development, automation, AI/ML workflows, or backend services
Working knowledge of machine learning and Generative AI concepts, including LLMs, embeddings, prompts, and RAG-based patterns
Comfortable working with SQL and enterprise datasets to support analytical and data-driven use cases
Good working knowledge of Google Cloud Platform or similar cloud environments
Experience developing or integrating APIs and backend or cloud-based applications
Ability to debug, test, and optimise AI/ML or data-driven solutions for accuracy, reliability, and performance
Communicate effectively with both technical and business stakeholders and work collaboratively across teams

Job Responsibility

Design, develop, and enhance GenAI and ML-based solutions for enterprise business use cases
Build and improve Natural Language to SQL and Retrieval-Augmented Generation based chatbot capabilities
Develop and maintain Python-based backend services, APIs, and AI workflows supporting LLM-driven applications
Work with Google Cloud Platform services to build, deploy, and optimise scalable, cloud-native AI applications
Improve retrieval quality, prompt orchestration, SQL generation accuracy, chatbot response quality, and overall solution performance
Collaborate with internal stakeholders to translate business requirements into scalable AI/ML product capabilities
Contribute to testing, evaluation, monitoring, documentation, and production-readiness of GenAI solutions
Support continuous improvement of AI engineering practices, including prompt design, evaluation frameworks, observability, and responsible AI usage

What we offer

Opportunity to work on production-grade Generative AI solutions with direct business impact
Hands-on exposure to modern GCP services, cloud-native deployment patterns, and enterprise-scale AI architectures
Experience across LLMs, RAG, Natural Language to SQL, enterprise data platforms, APIs, and chatbot engineering
A high-visibility role with clear growth pathways into senior engineering, technical leadership, solution architecture, or AI product ownership roles

Fulltime

Machine Learning Research Engineer, GenAI Applied ML

Lead applied ML engineering on Scale's Applied ML team, powering data infrastruc...

Location

United States , San Francisco; New York

Salary:

176000.00 - 220000.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

PhD or MSc in Computer Science, Mathematics, Statistics, or related field
3+ years shipping scaled production ML systems
Demonstrated real-world impact
Mastery of PyTorch, TensorFlow, JAX, or scikit-learn
Deep expertise in agentic LLMs and multi-agent systems
Strong software engineering and microservices (AWS/GCP)
Rapid, data-driven iteration
Proficiency using AI tools to accelerate work
Strong research depth with practical bias
Excellent cross-functional communication

Job Responsibility

Build and deploy multi-agent systems for agentic reasoning validation
Develop pipelines to detect errors and scale human judgment
Combine classical ML, LLMs, and multi-agent techniques for reliability
Lead research into agent failure modes and ship fixes
Use AI tools to speed prototyping and iteration
Build data-driven evaluations and deploy rapid improvements
Integrate systems into Scale's platform

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO

Fulltime

Machine Learning Research Engineer, Agents - Enterprise GenAI

The Enterprise ML Research Lab works on the front lines of this AI revolution. W...

Location

United States , San Francisco; New York

Salary:

218400.00 - 273000.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

1-3 years of building with LLMs in a production environment
Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
Publications in top conferences such as NEURIPS, ICLR, or ICML within the last two years
PhD or Masters in Computer Science or a related field

Job Responsibility

Train state of the art models, developed both internally and from the community, to deploy to our enterprise customers
Research cutting edge algorithms to integrate directly into our training stack
Build agents that leverage our proprietary agent-building algorithms to automatically hill climb datasets – including defining highly performant tools, multi-agent systems, and complex rewards

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO

Fulltime

Senior/Staff Machine Learning Engineer, General Agents, Enterprise GenAI

Scale AI is the data foundation for AI, helping organizations build and deploy r...

Location

United States , San Francisco; New York

Salary:

218000.00 - 273000.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

5+ years of experience building and deploying machine learning or AI systems for real-world, production use cases
Strong engineering fundamentals, supported by a Bachelor’s and/or Master’s degree in Computer Science, Machine Learning, AI, or equivalent practical experience
Deep understanding of modern LLMs, prompt-, context-, and system-level optimization, and agentic system design
Proven proficiency in Python, including writing production-quality, testable, and maintainable code
Experience building systems that integrate models with external tools, APIs, databases, and services
Ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints
Strong communication skills and comfort working in customer-facing or cross-functional environments

Job Responsibility

Design and implement end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases
Build scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints
Develop evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings
Collaborate closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs
Productionize frontier agent techniques (e.g., planning, multi-step reasoning and tool-use, multi-agent patterns) into maintainable, observable systems
Own deployment, monitoring, and iteration of agent systems, including failure analysis and continuous improvement based on real-world usage
Contribute to technical direction and architectural decisions for general agent development best practices and methods, with increasing scope and leadership at the Staff level

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO
equity based compensation
commuter stipend

Fulltime

Machine Learning Research Engineer, Agent Data Foundation - Enterprise GenAI

Join the team shaping the future of AI at Scale. The Enterprise ML Research Lab ...

Location

United States , San Francisco; New York

Salary:

218400.00 - 273000.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

3+ years of building with LLMs in a production environment
Clear experiences with constructing high quality data to use to improve an LLM/Agent
Publications in top conferences such as NEURIPS, ICLR, or ICML within the last two years
PhD or Masters in Computer Science or a related field

Job Responsibility

Build synthetic data pipelines to generate enterprise environments to use for RL post-training
Create agents to convert traces from production into actionable insights to use to improve agents
Contribute to our agent building product which can construct other agents using coding agents + proprietary algorithms
Train state of the art models, developed both internally and from the community, to deploy to our enterprise customers

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO
commuter stipend

Fulltime

Staff Machine Learning Research Engineer, Agent Post-training - Enterprise GenAI

Build out our next-gen Agent RL training platform; build out the platform that w...

Location

United States , San Francisco; New York

Salary:

218400.00 - 273000.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

5+ years of LLM training in a production environment
Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
Publications in top conferences such as NEURIPS, ICLR, or ICML within the last two years
PhD or Masters in Computer Science or a related field

Job Responsibility

Train state of the art models, developed both internally and from the community, to deploy to our enterprise customers
Research cutting edge algorithms to integrate directly into our training stack
Design solutions that enable complex multi-agent systems to directly learn from both process + outcome based rewards

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO
commuter stipend
equity based compensation

Fulltime

Machine Learning Systems Research Engineer, Agent Post-training - Enterprise GenAI

The Enterprise ML Research Lab works on the front lines of this AI revolution. W...

Location

United States , San Francisco; New York

Salary:

218400.00 - 273000.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

At least 1-3 years of LLM training in a production environment
Passionate about system optimization
Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
Ability to demonstrate know-how on how to operate the architecture of the modern GPU cluster
Experience with multi-node LLM training and inference
Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc.
Strong written and verbal communication skills to operate in a cross functional team environment
PhD or Masters in Computer Science or a related field

Job Responsibility

Build, profile and optimize our training and inference framework
Post-train state of the art models, developed both internally and from the community, to define stable post-training recipes for our enterprise engagements
Collaborate with ML teams to accelerate their research and development, and enable them to develop the next generation of models and data curation
Create a next-gen agent training algorithm for multi-agent/multi-tool rollouts

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO
additional benefits such as a commuter stipend
equity based compensation

Fulltime

Sr Machine Learning Engineer

Let’s do this. Let’s change the world. We are looking for a highly motivated exp...

Location

India , Hyderabad

Salary:

Not provided

Amgen

Expiration Date

Until further notice

Requirements

Hands-on experience with AWS, Databricks, Apache Spark, PySpark, SparkSQL, Python, and SQL for large-scale data engineering
Strong proficiency in workflow orchestration, Spark performance tuning, and scalable batch and streaming data pipeline development
Experience with real-time data processing and integration using Apache Kafka, Debezium, or similar streaming technologies
Hands-on experience with MLOps tools and practices, including MLflow, model serving, feature stores, experiment tracking, deployment, and lifecycle management
Experience with GenAI engineering practices, including prompt engineering, LLM evaluation, AI observability, agentic workflows, and knowledge graphs
Ability to design and develop APIs or service interfaces for data, ML, and GenAI application integration
Experience with Agile/SAFe delivery models, DevOps practices, CI/CD concepts, and cross-functional team collaboration
Strong analytical, problem-solving, debugging, communication, and teamwork skills
Ability to quickly learn, adapt, and apply emerging technologies across data, ML, and AI engineering
Doctorate degree / Master's degree / Bachelor's degree and 8 to 13 years of experience years of experience in Computer Science, IT or related field

Job Responsibility

Design, deploy, monitor, and optimize production-grade ML and Generative AI applications for AI-enabled manufacturing solutions
Define technical architecture, engineering standards, and best practices across data engineering, ML, GenAI, analytics, and platform capabilities
Partner with business stakeholders, product owners, and cross-functional teams to translate manufacturing challenges into secure, scalable, production-ready AI and data solutions
Design, develop, and maintain complex ETL/ELT pipelines in Databricks using PySpark, Scala, and SQL for large-scale structured and unstructured data processing
Build efficient ingestion, transformation, migration, and deployment pipelines across databases, APIs, logs, event streams, images, PDFs, documents, and third-party platforms
Design and implement GenAI solutions including RAG, embeddings, vector databases, agentic workflows, tool-calling systems, LLM orchestration, serving optimization, knowledge graphs, and metadata-driven retrieval
Build GenAI applications using frameworks and platforms such as LangChain, LangGraph, LlamaIndex, DSPy, OpenAI APIs, Amazon Bedrock, or equivalent technologies
Develop evaluation and observability frameworks to monitor model quality, hallucination rates, drift, retrieval effectiveness, latency, token usage, cost, reliability, operational health, and business impact
Build and maintain MLOps and LLMOps capabilities, including experiment tracking, model registry, prompt management, versioning, CI/CD, automated testing, deployment automation, monitoring, governance, and release controls
Design scalable data quality, validation, security, privacy, access control, logging, governance, and interoperability capabilities across hybrid cloud environments

Fulltime

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GenAI / Machine Learning Engineer

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