GenAI-Engineer

It's fun to work in a company where people truly BELIEVE in what they are doing! We're committed to bringing passion and customer focus to the business. 5-8 years of experience as a Data Scientist with deep expertise in GenAI, NLP/LLM technologies. The candidate should have hands-on experience ML model building as well as training and fine-tuning large language models from the ground up. This role requires a strong technical background in machine learning, deep learning, and practical experience with modern AI frameworks.  Responsibilities: - 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. Must-Have Skills: Generative AI & NLP: - SaaS-based LLMs: LangChain, LlamaIndex, vector databases, prompt engineering (CoT, ReAct, 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, Semantic Kernel. - Advanced Retrieval-Augmented Generation (RAG) systems: hybrid retrieval, knowledge graph–based retrieval, multi-hop RAG, hierarchical/contextual retrieval strategies, 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 on no-code/low-code app development. - MCP server, tools, skills and creation and maintenance of reusable componets. If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us! Not the right fit?  Let us know you're interested in a future opportunity by clicking  Introduce Yourself  in the top-right corner of the page or create an account to set up email alerts as new job postings become available that meet your interest!