Machine Learning Engineer

1 week ago


pune district, India Prana Tree Full time

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).



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