Gen AI 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.

Responsibilities

• Gen AI Engineer 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.
• hybrid retrieval, knowledge graph–based retrieval, multi-hop RAG, hierarchical/contextual retrieval strategies, evaluation/monitoring of RAG pipelines.
• MCP server, tools, skills and creation and maintenance of reusable components.

Requirements

• Generative AI & NLP: SaaS-based LLMs: LangChain, LlamaIndex, vector databases, prompt engineering (CoT, ReAct, agents), Azure OpenAI function calling, multimodal models.
• 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.
• Databricks Genie, Databricks AI/BI, AgentBricks MS Copilot Studio and knowledge on no-code/low-code app development.
• Document digitization and OCR methods.
• Behavioral Competencies Flexible to contribute to ad-hoc initiatives such as PoCs, solution prototyping, and proposal workflows.
• Open to working on non-GenAI AI/ML projects (e.g., computer vision, document digitization, data structuring, brainstorming for business use cases).
• Proactive in providing timely updates and driving tasks to completion.
• Demonstrates responsibility, accountability, curiosity, and an innovative mindset.
• Willingness to learn and understand the business context (e.g., Philips domain and data landscape) beyond core technical skills.
• If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

Preferred

• text classification, topic modeling, Q&A systems, conversational AI/chatbots, search, Document AI, summarization, content generation, and Named Entity Recognition (NER).
• Azure Document Intelligence or equivalent.

Tech Stack

Programming & Frameworks: Python, FastAPI Cloud & DevOps: Azure DevOps, Agile (Azure Boards) AI/ML Tools: Azure Databricks, MLFlow Model Lifecycle Management, Unity Catalog (Azure Databricks) Cloud Services: Azure Function Apps, Azure Blob Storage, Azure Cognitive Services, Azure AI Search Productivity Tools: Microsoft Copilot Studio (basic) Good-to-Have Skills: Ops & Engineering: AgentOps / LLMOps: Agent monitoring, evaluation, and debugging frameworks. LLM observability and tracing (LangSmith, LangFuse, Weights & Biases ). Prompt/version management and experimentation. Governance, compliance, and cost optimization for LLMs. CI/CD pipelines in Azure DevOps. Flask, Docker.

Azure Delta Lake.

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!