Senior/ Lead AI Engineer

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟰𝟮𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟲𝟬𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟰𝟮-𝟲𝟬 𝗟𝗣𝗔)

Experience: 5+ yrs

Location: Bengaluru

Job Type: Full-time

We are looking for an experienced Senior AI/ML Engineer – Generative AI to lead the design, development, and deployment of enterprise-scale AI solutions. The role focuses heavily on Generative AI, Large Language Models (LLMs), multimodal AI, agentic AI, RAG, and production machine learning systems .

The ideal candidate will combine strong hands-on engineering expertise with the ability to define AI/ML roadmaps, solve complex technical problems, and guide engineering teams. You will work closely with Business, Product, Engineering, Data Science, and MLOps teams to transform business challenges into scalable, secure, and production-ready AI solutions.

Requirements

Key Responsibilities

• Partner with Business, Product, Engineering, Data Science, and MLOps teams to design and deliver enterprise-scale AI solutions.
• Define and drive the AI/ML roadmap for key business and technology problem areas.
• Lead the design, prototyping, development, and production deployment of Generative AI and LLM-based applications .
• Work with models such as GPT, Claude, LLaMA, Mistral , and other foundation and multimodal models.
• Build scalable Retrieval-Augmented Generation (RAG) pipelines, embedding workflows, and retrieval architectures.
• Design and implement integrations with vector databases such as FAISS, Pinecone, Weaviate, and Milvus .
• Develop and optimise data pipelines supporting AI/ML applications and model workflows.
• Fine-tune models using approaches such as LoRA and PEFT and establish robust evaluation methodologies.
• Build AI orchestration and agentic workflows using frameworks such as LangChain and LlamaIndex .
• Optimise AI systems for latency, throughput, cost, scalability, accuracy, and reliability .
• Monitor model performance, drift, bias, and production behaviour and implement appropriate corrective measures.
• Design scalable ML deployment pipelines using cloud-native and containerised environments.
• Apply appropriate CI/CD, MLOps, observability, governance, and model lifecycle management practices.
• Collaborate with engineering teams to integrate AI capabilities into production applications and platforms.
• Lead technical debugging, root-cause analysis, performance optimisation, and production issue resolution.
• Establish best practices for experimentation, evaluation, documentation, security, and production readiness.
• Mentor and guide engineers while contributing to technical standards and AI/ML engineering practices.
• Evaluate emerging AI technologies and identify opportunities for their practical application.

What Makes You a Great Fit

• 5+ years of experience in AI/ML engineering, with strong hands-on experience delivering Generative AI solutions into production .
• Strong programming expertise in Python , with working knowledge of SQL and, where applicable, R.
• Strong experience with NumPy, Pandas, Scikit-learn , and other data science libraries.
• Hands-on expertise with deep learning frameworks such as PyTorch, TensorFlow, Keras, MXNet, or Caffe .
• Strong understanding of NLP, LLMs, multimodal AI, and modern Generative AI architectures .
• Experience with Hugging Face, Transformers, SpaCy, NLTK, Gensim, or Spark NLP .
• Proven experience building, integrating, evaluating, and fine-tuning LLMs .
• Strong knowledge of LangChain, LlamaIndex, RAG architectures, embeddings, and vector retrieval .
• Hands-on experience with Pinecone, FAISS, Weaviate, Milvus , or similar vector databases.
• Strong understanding of classical machine learning techniques, including regression, SVM, decision trees, random forests, and clustering.
• Experience with cloud ML platforms such as AWS SageMaker, Google Vertex AI, or Azure Machine Learning .
• Hands-on experience with Docker, Kubernetes, and cloud-native deployment environments .
• Strong knowledge of ML CI/CD, model observability, and governance tools such as MLflow, Weights & Biases, and LangSmith .
• Strong understanding of model evaluation, monitoring, scalability, security, cost optimisation, and production reliability.
• Excellent analytical and problem-solving skills with the ability to tackle complex AI/ML challenges.
• Strong technical leadership, communication, stakeholder-management, and mentoring abilities.
• Bachelor's, Master's, or PhD in Computer Science, Mathematics, Statistics, Engineering, or a related discipline from a recognised institution is preferred.