About the role
Responsibilities:
- 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, and 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, and 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 the 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.
Requirements:
- 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, and R and experience with data science libraries such as NumPy, Pandas, MatLab, and scikit-learn.
- Proven experience with deep learning frameworks: PyTorch, TensorFlow, Keras, MXNet, Caffe.
- Familiarity with NLP and ML libraries: Transformers, SparkNLP, Gensim, SpaCy, NLTK, and 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, and Azure ML) and containerization (Docker and Kubernetes).
- Applied knowledge of classical ML algorithms (SVM, Decision Trees, Random Forests, regression, and clustering) alongside modern DL/GenAI approaches.
- Strong knowledge of CI/CD for ML, model observability (MLflow, Weights and Biases, LangSmith), and governance frameworks.
- Excellent problem-solving skills, communication, and ability to lead technical teams.