Experience: 4–5 Years
We are seeking a Senior GenAI Engineer who can design, build, and deploy production-grade Generative AI solutions. This role combines hands-on GenAI development (LLMs, RAG, prompt engineering) with practical ML engineering and deployment skills.
This is not a pure research role and not a pure DevOps role. The ideal candidate is comfortable taking an AI use case from idea → prototype → production, working closely with product and engineering teams.
Key Responsibilities: Generative AI & Modeling
- Design and implement LLM-powered applications using models such as GPT, LLaMA, Claude, or similar
- Build Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases
- Perform prompt engineering, prompt optimization, and evaluation
- Fine-tune LLMs using PEFT / LoRA techniques where required
- Evaluate model outputs for quality, hallucination, latency, and cost
Deployment & ML Engineering
- Deploy models as REST APIs using frameworks such as FastAPI or Flask
- Containerize AI services using Docker
- Build and maintain ML pipelines for training, inference, and monitoring
- Implement basic model monitoring and drift detection
- Optimize inference performance pragmatically (latency, throughput, cost)
Collaboration & Ownership
- Work closely with product managers to translate business problems into AI solutions
- Collaborate with backend and platform teams for integration and deployment
- Take ownership of AI components in production, including reliability and maintainability
- Stay current with emerging trends in GenAI and applied AI systems
Required Skills & Qualifications
- 4–6 years of experience in Machine Learning / Data Science / GenAI
- Strong proficiency in Python
- Hands-on experience with PyTorch or TensorFlow
- Practical experience working with LLMs and GenAI systems
- Experience building RAG pipelines using vector databases such as FAISS, Pinecone, Weaviate, or ChromaDB
- Solid understanding of prompt engineering and LLM evaluation
- Experience deploying models using REST APIs
- Working knowledge of Docker
- Strong problem-solving and analytical skills
Cloud & Infrastructure (Working Knowledge)
- Experience deploying AI models on cloud platforms (Azure preferred)
- Familiarity with Kubernetes (deployment and scaling basics)
- Understanding of ML lifecycle concepts (training, inference, monitoring)
Good-to-Have Skills
- Experience with LangChain, LlamaIndex, or Hugging Face
- Exposure to on-prem or GPU-based deployments
- Experience with MLOps tools (MLflow, Airflow, Kubeflow, DVC)
- Knowledge of model optimization techniques (quantization, batching)
- Familiarity with CI/CD pipelines for ML systems
Ideal Candidate Profile
- T-shaped engineer: deep in GenAI + broad in ML engineering
- Comfortable working in ambiguity and early-stage problem spaces
- Bias toward practical, production-ready solutions over academic perfection
- Strong communicator who can explain AI trade-offs to non-technical stakeholders
Education
- Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related field (or equivalent practical experience)
