Lead AI Engineer
- Posted 5 months ago
- Be among the first 10 applicants
Job Description
Role: Lead AI Engineer
Experience: 8+ yrs
Location: MG Road, Bangalore
Work Mode: Hybrid (3 days from office)
About the Role
We are seeking a highly skilled Lead AI Engineer to architect, build and scale AI-powered solutions. The ideal candidate will guide the AI development lifecycle—research, model building, optimization, deployment—and lead a team of ML/GenAI engineers to deliver high-impact intelligent systems. You will work closely with product, data and engineering teams to define strategy, ensure delivery excellence and drive innovation across AI initiatives.
Key Responsibilities
- Lead design and development of AI/ML and Generative AI solutions end-to-end.
- Build, train, fine-tune and optimize deep learning models (NLP, Vision, LLMs, RAG, multimodal).
- Architect scalable pipelines for data processing, model training and real-time inference.
- Evaluate, fine-tune and integrate LLMs (OpenAI, Llama, Gemini, Claude etc.)
- Implement RAG pipelines, prompt engineering and vector search for enterprise AI apps.
- Deploy models on cloud platforms (AWS/Azure/GCP) using MLOps best practices.
- Manage and mentor AI/ML engineers, perform code and architecture reviews.
- Partner with stakeholders to translate business requirements into technical solutions.
- Research emerging AI trends, POCs and drive innovation programs.
- Ensure responsible AI practices—security, bias mitigation, model monitoring.
Required Skills
- Strong expertise in Python, ML frameworks: PyTorch, TensorFlow, Hugging Face.
- Hands-on experience building end-to-end ML/GenAI systems for production.
- Deep understanding of NLP, LLMs, embeddings, transformers, vector DBs.
- Experience with RAG, prompt engineering, agent frameworks (LangChain/LlamaIndex).
- Cloud + MLOps skills: Docker, Kubernetes, CI/CD, model deployment, monitoring.
- Strong grasp of data engineering concepts: ETL, feature store, data pipelines.
- Familiarity with distributed training frameworks (Ray, DeepSpeed, Horovod) is a plus.
- Ability to lead technical discussions, architecture planning & delivery.
Good to Have
- Experience training custom LLMs or multimodal models at scale.
- Knowledge of DataBricks, MLflow, Vertex AI, SageMaker.
- Publications, patents, Kaggle ranks or open-source contributions.
- Domain experience in Healthcare, FinTech, SaaS or Enterprise AI products.
Education
- Bachelor's/Master's degree in Computer Science, AI, Data Science or related field.
Why Join Us
- Work on cutting-edge AI product innovations.
- Ownership, autonomy, and opportunity to build & scale AI teams.
- Competitive compensation with growth opportunities.
More Info
Key Skills
LLMs
feature store
vector DBs
embeddings
data pipelines
RAG
prompt engineering
About Company
Skills:
Java, Kafka, Spring Boot, Datadog, Redis, Postgres, Helm, Kubernetes, AWS, LangChain, LangGraph, Temporal
Skills:
Orchestration, Power Platform, Classical statistics, Tool Function Calling, Generative AI, ML feature engineering, Vector Search, AI applications, structured outputs, model evaluation, Agentic AI, failure handling, Embeddings, experimentation, RAG, Azure AI services, Prompt Engineering, Copilot Studio
Skills:
Numpy, Pandas, Gcp, FastAPI, Azure, Python, AWS, GenAI, embeddings, LLMs, prompt engineering, RAG
Skills:
isolation), vLLM), Open-source LLMs (Ollama, Security fundamentals (auth, Cloud deployment, Docker, FastAPI, Python, LangChain, Re-ranking models, Vector databases, LangGraph, secrets, Voice AI pipelines, Hybrid search, CI/CD, RAG pipelines, Multi-agent systems, LlamaIndex
Skills:
Python, LangChain, LLMs Prompt Engineering, Custom Guardrails, Vector Embeddings Semantic Search, MLflow, Multi-Agent Orchestration, LLM Evals, LangSmith, LangGraph, AI Observability Tools, RAG, Agent Engineering