Solution Engineer (GenAI Specialist)

Experience: 2–5 Years

Role Summary

Responsible for developing GenAI-powered applications (RAG, chatbots, APIs, multimodal solutions), integrating backend modules, optimizing LLM performance, and supporting client PoCs and demos.

Key Responsibilities

• - Build GenAI applications including RAG pipelines, chatbots, semantic search, and multimodal workflows.
• - Develop and optimize backend APIs using Python (FastAPI/Flask).
• - Integrate AI modules with SQL/NoSQL databases & external APIs.
• - Implement prompt engineering strategies and evaluate model outputs.
• - Set up evaluation pipelines using RAGAS / TruLens.
• - Support pre-sales, client demos, and rapid PoC development.
• - Improve performance, reduce token cost, and ensure solution scalability.

Required Skills

• - Strong Python and backend API development skills.
• - Experience with LangChain / LlamaIndex / Haystack.
• - Knowledge of vector databases (Pinecone, Weaviate, Milvus, pgvector).
• - Hands-on with LLM APIs (OpenAI, Azure OpenAI, Claude, Gemini).
• - Understanding of RAG architecture, hybrid search & re-ranking.
• - SQL + NoSQL (MongoDB / DynamoDB) fundamentals.
• - Basic ML (Regression, Classification, Clustering).
• - Computer Vision basics (OpenCV).

Technical & Deployment Skills

• - Docker/Kubernetes basics for deployments.
• - CI/CD & version control for code/prompt updates.
• - Optional: Fine-tuning (LoRA/PEFT), multimodal LLMs.

Business & Soft Skills

• - Ability to translate business requirements into technical GenAI solutions.
• - Understanding of token/cost optimization & security considerations.
• - Strong collaboration with analysts, engineers, and project leads.
• - Good communication for presenting PoCs and solution walkthroughs.

Preferred (Optional)

• - GenAI / LLM Certifications (DeepLearning.AI, Azure, AWS).
• - Exposure to domain-specific use cases (support automation, finance, retail).