GenAI-Engineer

Overview

Design, build and deploy advanced GenAI and LLM-based solutions, taking end-to-end ownership of AI initiatives and their business impact.

What you'll do

• Design, develop, and implement advanced solutions leveraging Large Language Models (LLMs).
• Take full ownership of initiatives, delivering end-to-end solutions with minimal supervision.
• Stay current with the latest advancements in Generative AI, LLMs, RAG systems, and applied research.
• Build and maintain reusable code libraries, tools, and frameworks to accelerate AI development.
• Participate in code reviews to ensure high-quality, maintainable, and scalable solutions.
• Contribute across the entire software development lifecycle: design, implementation, testing, deployment, and maintenance.
• Collaborate with cross-functional teams to align AI solutions with business goals, integrate contributions into core systems, and influence roadmaps.
• Apply strong analytical and problem-solving skills to design efficient solutions for complex business challenges.
• Communicate effectively across technical and non-technical teams, ensuring transparency and alignment.
• Own business impact of AI solutions, including adoption, accuracy, latency, and cost efficiency.
• Translate ambiguous business problems into structured AI solution approaches and measurable outcomes.
• Drive solution success metrics (e.g., productivity gains, automation %, decision accuracy).
• Engage directly with business and technical stakeholders to understand requirements, present solutions, and influence decision-making.
• Communicate solution architecture and trade-offs clearly to both technical and non-technical audiences.
• Contribute to client discussions, PoCs, and proposal development.
• Design scalable, modular, and production-grade AI systems (APIs, pipelines, orchestration layers).
• Define architecture patterns for LLM applications (RAG pipelines, agentic workflows, hybrid systems).
• Make trade-offs across latency, cost, accuracy, and maintainability.
• Build reusable accelerators, frameworks, and components that can be leveraged across multiple use cases and clients.
• Contribute to internal IP creation (assets, templates, reference architectures).
• Ensure reliability and robustness of LLM systems through evaluation frameworks, guardrails, and fallback strategies.
• Design safe and responsible AI systems (hallucination mitigation, bias handling, governance).
• Optimize cost-performance trade-offs in large-scale deployments.
• Identify when NOT to use LLMs and propose alternative approaches.
• Contribute to code reviews, design reviews, and mentorship of junior team members.
• Drive quality standards and best practices across projects.
• Stay ahead of advancements in GenAI and proactively evaluate their applicability to business problems.
• Contribute to internal knowledge sharing, training, and capability building.

What you'll need

• 5-8 years of experience as a Data Scientist with deep expertise in GenAI and NLP/LLM technologies.
• Hands-on experience in ML model building, including training and fine-tuning large language models from the ground up.
• Strong technical background in machine learning and deep learning.
• Practical experience with modern AI frameworks.
• Generative AI and NLP: SaaS-based LLMs (LangChain, LlamaIndex, vector databases, prompt engineering such as CoT, ReAct, and agents, Azure OpenAI function calling, multimodal models).
• Open-source and SaaS LLMs: Azure OpenAI, Claude Opus 4.6, GPT-3.5 Turbo, GPT-4, etc.
• At least one agentic Generative AI framework: CrewAI, AutoGen, LangGraph, n8n, LangFlow, SmolAgents, or Semantic Kernel.
• Advanced Retrieval-Augmented Generation (RAG) systems: hybrid retrieval, knowledge graph–based retrieval, multi-hop RAG, hierarchical/contextual retrieval strategies, and evaluation/monitoring of RAG pipelines.
• Classical NLP: text classification, topic modeling, Q&A systems, conversational AI/chatbots, search, Document AI, summarization, content generation, and Named Entity Recognition (NER).
• Databricks ecosystem: Databricks Genie, Databricks AI/BI, AgentBricks.
• MS Copilot Studio and knowledge of no-code/low-code app development.
• MCP server, tools, skills, and creation and maintenance of reusable components.

Details

• Location: Bengaluru.