Senior Generative AI Architect

Job Title/Role: Senior Generative AI Architect We at Coforge are hiring an experienced Senior Generative AI Architect with strong expertise in Generative AI, cloud-native architecture. The ideal candidate will have a hands‑on background in Generative AI solutions, system design, and AI‑driven customer experience solutions, with the ability to design, build, and scale intelligent solutions for enterprise clients. Key Responsibilities Fine‑Tuning Experience. Iterative training approach: number of cycles, convergence criteria, and evaluation metrics. Rationale for selecting model architecture (Transformer vs. classical ML approaches). Understanding of pre‑trained models vs. custom models. Choice of loss functions and their business/technical rationale. Gradient‑related challenges (vanishing/exploding gradients) and mitigation techniques. Handling imbalanced datasets (resampling, weighting, synthetic data generation). Ranking mechanisms (e.g., low‑rank adaptations, embedding ranking logic). Managing model drift (data drift, concept drift detection and remediation strategies). Model evaluation, monitoring, and retraining pipelines. Observability and feedback loops (model metrics, user feedback integration). Deployment validation and A/B testing approaches. Agentic AI & LLM Application Design Zero‑shot vs. few‑shot prompting strategies and when to use each. Prompt engineering and prompt fine‑tuning techniques. Frameworks & Libraries Experience with agentic AI frameworks (e.g., LangChain, Semantic Kernel, AutoGen, CrewAI). Integration patterns for tool usage and orchestration. Techniques to manage context windows effectively. Retrieval‑Augmented Generation (RAG) design and optimization. Cost optimization strategies (prompt compression, chunking, caching). Trade‑offs between latency, cost, and accuracy. Design of self‑healing systems (retry logic, fallback strategies, tool re‑planning). Memory management (short‑term vs. long‑term; local vs. global memory). Best practices in agent orchestration and modular design. Codebase & Project Structure Ideal structure for scalable AI/agentic applications. Separation of concerns (prompts, tools, memory, orchestration layers). LLM Observability & Data Architecture LLM Observability Instrumentation and logging strategies. Vector Databases & Retrieval Experience with vector DBs (e.g., Pinecone, FAISS, Weaviate, Azure AI Search). Embedding strategies and indexing mechanisms. Distance/similarity metrics (cosine similarity, Euclidean, dot product) and use cases. Combining Vector DBs with Relational DBs (structured data). Metadata stores (filtering, search refinement). Designing efficient retrieval pipelines. MCP / Orchestration Layer Understanding and application of MCP concepts in AI systems. Managing communication between models, tools, and services. Deployment Strategies Containerization (Docker/Kubernetes) and cloud deployment (Azure/AWS/GCP). Scalability & Reliability Load handling, auto‑scaling, and failover mechanisms. Performance optimization in production environments. Good to Have Designing test strategies for deterministic and non‑deterministic (AI) systems. Establishing measurable benchmarks for LLM performance and system reliability. #J-18808-Ljbffr