Gen AI/ML Senior Engineer
10 hours ago
Key Responsibilities
- Design, build, and productionize scalable ML/GenAI models and pipelines with a focus on reliability and precision
 - Develop retrieval-augmented generation (RAG) pipelines and agentic systems using advanced LLMs
 - Fine-tune, optimize, and deploy transformer-based models (GPT, Llama, Mistral, etc.) for enterprise use cases
 - Architect and implement multi-agent frameworks (tool-augmented reasoning, orchestration, memory management)
 - Deliver robust APIs and services for ML/GenAI models with performance and cost optimization in mind
 - Collaborate with software engineers and DevOps teams to scale AI systems across environments (cloud, hybrid, on-prem)
 - Drive automation in model deployment, monitoring, retraining, and drift detection for continuous improvement
 - Ensure security, compliance, and governance while delivering high-value AI solutions
 - Stay ahead of GenAI, LLM, and infrastructure innovations, bringing applied advancements into the product roadmap
 
Required Skills and Qualifications
- Bachelors/Masters degree in Computer Science or related field
 - Proven experience in delivering production-grade ML/GenAI applications
 - Strong proficiency in Python (3.x) with ML/DL frameworks (PyTorch, TensorFlow, Hugging Face)
 - Hands-on experience with LLMs and fine-tuning methods (LoRA, PEFT, quantization)
 - Deep expertise in RAG pipelines, embeddings, and vector databases (Elastic , Pinecone, Milvus etc)
 - Applied knowledge of GenAI frameworks (LangChain, LlamaIndex, Haystack, LangGraph, AutoGen, )
 - Strong understanding of Agent-based systems (multi-agent orchestration, tool use, reasoning loops)
 - Familiarity with Model Context Protocol (MCP) for secure and interoperable GenAI integrations
 - Experience with cloud-native ML engineering (Azure OpenAI, AWS Bedrock) and container orchestration (Docker/Kubernetes)
 - Experience with Responsible AI practices, including transparency, and ethical use of GenAI models.
 - Proficiency with MLOps practices: CI/CD for ML, monitoring, retraining, and scaling
 
Immediate Joiners or Less Notice Period are preferable
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