Job Title : ML Engineer
Location : Chennai / Hyderabad / Pune
Work Mode : Work from Office
Experience : 4–8 Years
About the role :
We are seeking a highly skilled Machine Learning Engineer with strong experience in building, deploying, and optimizing AI / ML systems—especially those leveraging LLMs, NLP, GenAI, and cloud-native services. The ideal candidate will have hands-on expertise with AWS (including Bedrock), modern vector databases such as BetaDB, and production-grade ML orchestration.
Key Responsibilities :
- Design, develop, and deploy end-to-end machine learning pipelines on AWS.
- Build and fine-tune LLM-based applications for tasks such as summarization, generation, semantic search, classification, and agent-based workflows.
- Implement NLP solutions including text preprocessing, embedding pipelines, retrieval systems, and conversational AI.
- Utilize AWS Bedrock for model hosting, inference orchestration, prompt engineering, evaluation, and optimization.
- Manage knowledge bases and vector storage using BetaDB or similar platforms for RAG and generative workflows.
- Develop GenAI architectures, including prompt workflows, agents, RAG pipelines, and evaluation frameworks.
- Integrate ML models into production systems using scalable APIs, microservices, and CI / CD pipelines.
- Work cross-functionally with product, engineering, and data teams to deliver high-impact AI solutions.
- Ensure system reliability, observability, and performance tuning for real-time and batch inference workloads.
Required skills & experience :
4–8 years of experience as an ML Engineer / AI Engineer / NLP Engineer.Strong expertise in :Python, PyTorch / TensorFlow, LangChain, or related ML frameworks.LLMs (OpenAI, Anthropic, Cohere, or OSS like Llama, Mistral).GenAI architectures (RAG, agents, orchestrators, evaluators).NLP techniques : embeddings, vector search, tokenization, entity extraction, topic modeling, transformers.Hands-on experience with :
AWS Cloud Services (SageMaker, Lambda, SQS, DynamoDB, ECS / EKS, API Gateway).AWS Bedrock for model hosting, tuning, and orchestration.BetaDB (or similar vector DBs : Pinecone, Weaviate, Milvus).Strong understanding of ML system design, inference optimization, caching, latency management, and API integration.Experience with MLOps / DevOps tools (Docker, GitHub Actions, Terraform, Kubernetes preferred).