You will work cross-functionally with data scientists, software engineers, risk and compliance teams, and business stakeholders to define reference architectures, integrate with existing systems, and ensure secure, ethical, and compliant AI adoption.
Architecture & Solution Design
- Design scalable and modular architectures for generative AI solutions (LLMs, chatbots, copilots, document intelligence, image/video generation, etc.).
- Define integration patterns between LLM platforms (e.g., OpenAI, Azure OpenAI, Anthropic, Google Gemini) and enterprise systems (e.g., SAP, ServiceNow, Salesforce).
- Build reference architectures, reusable components, and frameworks for GenAI use cases.
- Select and benchmark foundational models and fine-tuning strategies (RAG, LoRA, adapter-based tuning).
Technical Leadership
- Drive end-to-end solution development from ideation to deployment.
- Develop proof-of-concepts and pilots to demonstrate ROI and feasibility.
- Collaborate with data engineers to design data pipelines and vector stores for retrieval-augmented generation (RAG).
- Guide MLOps and AIOps teams on model lifecycle management, prompt orchestration, versioning, and observability.
- Lead the evaluation and adoption of GenAI platforms, APIs, and orchestration tools (Microsoft AutoGen, LangChain, Semantic Kernel, etc.).
- Define guardrails for prompt safety, hallucination mitigation, and explainability
Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- 10+ years of total experience, including 4+ years in AI/ML solution architecture or enterprise data systems.
- Proven experience designing or deploying solutions using LLMs.
- Strong programming and architecture skills in Python, API design, and microservices.
- Experience with Azure AI Studio, AWS Bedrock, or Google Vertex AI.
- Hands-on experience with vector databases and prompt engineering.
- Familiarity with DevOps/MLOps tools (Docker, Kubernetes, MLflow, Databricks, Azure ML, etc.).
Preferred Skills and Experience:
- Experience in building enterprise AI copilots or domain-specific LLM agents.
- Familiarity with frameworks like LangChain, LlamaIndex, Semantic Kernel, or Haystack.
- Strong stakeholder communication and the ability to translate technical complexity into business value.
- Experience with multimodal models (text, image, voice, video).
- Strong analytical and problem-solving mindset.
- Ability to operate in a highly cross-functional environment.
- Strategic thinking with hands-on execution ability.
- Excellent communication and presentation skills.