Lead AI Engineer

Roles & Responsibility :

Partner with business, product, and engineering stakeholders to design and implement enterprise-scale AI solutions, with a strong emphasis on Generative AI applications (LLMs, multimodal, agentic AI).

Define and own the AI/ML roadmap for key problem areas, balancing near-term delivery with long-term innovation.

Lead design, prototyping, and deployment of Generative AI models (GPT, Claude, LLaMA, Mistral, Stable Diffusion) for production use cases.

Build and optimize data pipelines, retrieval-augmented generation (RAG) systems, embedding strategies, and integrations with vector databases (FAISS, Pinecone, Weaviate, Milvus).

Ensure robust model training, fine-tuning (LoRA, PEFT), orchestration (LangChain, LlamaIndex), monitoring, and governance.

Lead debugging and optimization of AI systems for latency, throughput, cost, and model drift/bias.

Collaborate with ML engineers, data scientists, and MLOps teams to design scalable deployment pipelines using modern cloud and containerized environments.

Mentor and guide engineers, setting best practices for experimentation, evaluation, and production readiness.

Keep abreast of latest AI/ML research in LLMs, CV, NLP, and multimodal learning, driving adoption of cutting-edge methods.

Translate complex AI concepts into business outcomes for non-technical stakeholders.

Required Skills :

5–10 years of experience in AI/ML engineering, with at least 3+ years delivering Generative AI models into production.

Bachelor’s/Master’s/PhD in Computer Science, Mathematics, Statistics, or related field from a top-tier institution IITs/NITs/BITs etc.

Strong applied programming skills in Python, SQL, R and experience with data science libraries such as NumPy, Pandas, MatLab, scikit-learn.

Proven experience with deep learning frameworks: PyTorch, TensorFlow, Keras, MXNet, Caffe.

Familiarity with NLP and ML libraries: Transformers, SparkNLP, Gensim, SpaCy, NLTK, Hugging Face.

Experience building and fine-tuning LLMs and integrating them with orchestration frameworks (LangChain, LlamaIndex).

Expertise with vector databases (Pinecone, FAISS, Weaviate, Milvus) and knowledge of embedding retrieval patterns.

Cloud-native ML experience (AWS Sagemaker, GCP Vertex AI, Azure ML) and containerization (Docker, Kubernetes).

Applied knowledge of classical ML algorithms (SVM, Decision Trees, Random Forests, regression, clustering) alongside modern DL/GenAI approaches.

Strong knowledge of CI/CD for ML, model observability (MLflow, Weights & Biases, LangSmith), and governance frameworks.

Excellent problem-solving skills, communication, and ability to lead technical teams.