Gen AI/ML Senior Engineer

7 days ago

Gurugram Kolkata, India Ericsson Full time ₹ 20,00,000 - ₹ 25,00,000 per year

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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