● 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.
Required Skills :
● Experience in AI/ML engineering, with at least 5+ 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.
