AI/ML Engineer

5 days ago

Chennai, Tamil Nadu, India Anubavam Full time ₹ 15,00,000 - ₹ 25,00,000 per year

Key Responsibilities:

LLM Deployment & Optimization

  • Deploy, fine-tune, and optimize open-source LLMs (e.g., LLaMA, Mistral, CodeS, DeepSeek).
  • Implement quantization (e.g., 4-bit, 8-bit) and pruning for efficient inference on commodity hardware.
  • Build and manage inference APIs (REST/gRPC) for production use.

Infrastructure Management

  • Set up and manage on-premise GPU servers and VM-based deployments.
  • Build scalable cloud-based LLM infrastructure using AWS (SageMaker, EC2), Azure ML, or GCP Vertex AI.
  • Ensure cost efficiency by choosing appropriate hardware and job scheduling strategies.

MLOps & Reliability Engineering

  • Develop CI/CD pipelines for model training, testing, evaluation, and deployment.
  • Integrate version control for models, data, and hyperparameters.
  • Set up logging, tracing, and monitoring tools (e.g., MLflow, Prometheus, Grafana) for model performance and failure detection.

Security, Compliance & Performance

  • Ensure data privacy (FERPA/GDPR) and enforce security best practices across deployments.
  • Apply secure coding standards and implement RBAC, encryption, and network hardening for cloud/on-prem.

Cross-functional Integration

  • Work closely with AI solution engineers, backend developers, and product owners to integrate LLM services into the platform.
  • Support performance benchmarking and A/B testing of AI features across modules.

Documentation & Internal Enablement

  • Document LLM pipelines, configuration steps, and infrastructure setup in internal playbooks.
  • Create guides and reusable templates for future deployments and models.

Key Requirements:

Education:

  • Bachelors or Masters in Computer Science, AI/ML, Data Engineering, or related field.

Technical Skills:

  • Strong Python experience with ML libraries (e.g., PyTorch, Hugging Face Transformers).
  • Familiar with LangChain, LlamaIndex, or other RAG frameworks.
  • Experience with Docker, Kubernetes, and API gateways (e.g., Kong, NGINX).
  • Working knowledge of vector databases (FAISS, Pinecone, Qdrant).
  • Familiarity with GPU deployment tools (CUDA, Triton Inference Server, HuggingFace Accelerate).

Experience:

  • 4+ years in an AI/MLOps role, including experience in LLM fine-tuning and deployment.
  • Hands-on work with model inference in production environments (both cloud and on-prem).
  • Exposure to SaaS and modular product environments is a plus.

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