Key Responsibilities A. LLM & Generative AI Solution Build • Design and build LLM-powered components — RAG pipelines, document intelligence, summarisation, classification, extraction, and conversational agents — across multiple solution concepts in parallel. • Develop agentic workflows using tool calling, multi-step orchestration, and clear guardrails and fallback behaviour. • Engineer prompts, system instructions, and structured output schemas; version and test them like code. • Select the right model for each task across commercial APIs (Anthropic, OpenAI, Google, Azure OpenAI, AWS Bedrock) and open-weight models, balancing quality, latency, and cost. B. Data, Retrieval & Model Development • Build ingestion pipelines for client data: document parsing (PDFs, scans, spreadsheets), chunking strategies, embedding generation, and metadata enrichment. • Design and tune retrieval — vector search, hybrid (keyword + semantic) search, re-ranking, and query rewriting. • Build, train, and evaluate classical ML models (classification, forecasting, anomaly detection) where the problem calls for them rather than an LLM. • Assess client data readiness during discovery and flag quality, volume, or privacy gaps early. • Fine-tune or adapt models (e.g. LoRA) only when prompting and retrieval are not enough, backed by a clear cost-benefit case. C. Evaluation, Quality & Cost Control • Build evaluation harnesses for every AI component: golden datasets, automated metrics, LLM-as-judge scoring, and human review loops. • Measure and reduce hallucinations, retrieval misses, and edge-case failures before anything goes in front of a client. • Track token usage, latency, and cost per transaction; provide running-cost inputs for solution pricing and client ROI models. • Implement guardrails: PII redaction, prompt injection defences, content filtering, and output validation. D. Deployment, MLOps & Collaboration • Package AI services as clean APIs (FastAPI or equivalent) that the Full Stack Engineer can integrate without friction. • Containerise and deploy AI services to cloud platforms (AWS, Azure, GCP); monitor quality drift, latency, and cost in live environments. • Support the AI Solutions Lead in pre-sales — assess technical feasibility, answer model and data questions, and contribute architecture notes to proposals. • Maintain a reusable library of retrieval modules, evaluation scripts, prompt templates, and agent patterns so each new engagement starts further along. • Document model choices, evaluation results, and known limitations for every build. Requirements Mandatory Technical Requirements The following are non-negotiable for this role: • Python: Strong production-grade Python — clean, typed, tested code; async patterns; dependency and environment management. [MANDATORY] • LLM Application Development: Hands-on experience building on LLM APIs (Anthropic, OpenAI, Google, or Azure OpenAI) — prompt design, tool/function calling, structured outputs, streaming, and token and cost management. [MANDATORY] • RAG & Vector Databases: At least one retrieval-augmented system built end to end — chunking, embeddings, vector stores (Pinecone, Qdrant, Chroma, pgvector, or similar), and retrieval tuning. [MANDATORY] • ML Fundamentals: Solid grounding in supervised learning, evaluation metrics, overfitting, and embeddings, with hands-on use of scikit-learn and PyTorch or TensorFlow. [MANDATORY] • AI Evaluation: Demonstrated practice of measuring AI output quality with test sets and metrics — not just manual spot-checks. [MANDATORY] • API Development & Deployment: Ability to expose models as REST APIs, containerise with Docker, and deploy to a cloud platform; proficiency with Git. [MANDATORY] Strongly Preferred • Orchestration Frameworks: LangChain, LangGraph, LlamaIndex, or equivalent; experience with agent frameworks and the Model Context Protocol (MCP). • Document AI: OCR and document parsing (Azure Document Intelligence, AWS Textract, Unstructured, or similar) for messy enterprise documents. • Cloud AI Platforms: AWS Bedrock / SageMaker, Azure AI Foundry, or Google Vertex AI. • Observability: LLM tracing and evaluation tools such as LangSmith, Langfuse, Arize, or Weights & Biases. • Data Engineering: SQL, pandas, and building reliable batch data pipelines. Advantageous Not required, but a clear differentiator for this role: • Voice AI experience — speech-to-text, text-to-speech, and real-time voice agent pipelines with telephony integration. • Fine-tuning and serving open-weight models (Llama, Mistral, Qwen) with vLLM, TGI, or Ollama. • Knowledge graphs, graph-based retrieval, or text-to-SQL systems over enterprise data. • Computer vision or multimodal model experience. • Awareness of data protection requirements (GDPR, HIPAA, India's DPDP Act) and how they shape AI solution design. What We Look For (Beyond the Stack) • Evidence over enthusiasm — you trust an evaluation score more than a good-looking demo. • Pragmatism in model choice: you reach for the simplest approach that works, whether that is a prompt, a classifier, or a rule. • Cost awareness — you think about what a solution costs to run at 10,000 requests a day, not just whether it works once. • Ability to explain AI behaviour, limits, and risks in plain language to non-technical colleagues and clients. • A GitHub profile, Kaggle record, published work, or side projects that show what you build when nobody assigns it. Qualifications • Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, Statistics, Engineering, or equivalent practical experience. • 4 – 7 years of hands-on experience in ML or software engineering, including at least 2 years building LLM or Generative AI applications. • At least one AI solution taken from prototype to live deployment with real users. • Prior experience in an AI product company, an IT services AI practice, a startup, or an innovation lab is a plus. Benefits What We Offer • Variety — you will build across multiple industries and AI use cases rather than tuning one model forever. • Direct line of sight from your models to a real client decision. • Access to current commercial and open-weight models, with freedom to pick the right tool for each problem. • Mentorship from the AI Solutions Lead and exposure to enterprise solutioning and pre-sales. • Learning budget for AI/ML upskilling, conferences, and cloud certifications. • Competitive compensation with a clear path toward Senior AI Engineer or AI Solution Architect tracks