Senior AI Engineer

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, 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 5+ years delivering generative AI models into production.
• Bachelor's/Master's/PhD in Computer Science, Mathematics, Statistics, or a 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, and Caffe.
• Familiarity with NLP and ML libraries: Transformers, spaCy, Gensim, 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, Azure ML) and containerization (Docker, 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 & Biases, LangSmith), and governance frameworks.
• Excellent problem-solving skills, communication, and ability to lead technical teams.