Job Description

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 disciplinefrom a recognised institution is preferred.