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!