Senior Generative AI Architect

Coforge; Atlanta (GA); On-site; USD 180,000 - 240,000; Full time

Job summary

Coforge is seeking a Senior Generative AI Architect in Atlanta, GA, to design and scale enterprise AI solutions. The role emphasizes hands-on work with Generative AI, architecture decisions, and end-to-end AI delivery for customer experiences.

The ideal candidate will lead model selection, tooling integration (LangChain, LLM frameworks), and scalable deployment strategies in a cloud-native environment, ensuring observability and ongoing optimization.

Qualifications

• Hands-on experience with Generative AI architectures and enterprise deployments.
• Proven ability to design and scale AI-driven customer experiences.
• Experience with cloud-native architectures and containerized deployments.

Responsibilities

• Fine-tune models and define training iterations with clear convergence criteria.
• Assess model architectures (Transformer vs classical ML) and justify choices.
• Distinguish pre-trained vs. custom models and select appropriate approaches.
• Define loss functions and rationales; address gradient issues.
• Handle data imbalance with resampling or synthetic data; ensure robustness.
• Implement ranking mechanisms and monitor model drift; plan remediation.
• Design monitoring, evaluation, and retraining pipelines; establish observability.
• Plan deployment validation and AB testing strategies; optimize latency, cost, and accuracy.

Skills

Generative AI

Model fine-tuning

Prompt engineering

LLM design

RAG

LangChain

Semantic Kernel

AutoGen

CrewAI

Vector DBs

Pinecone

FAISS

Weaviate

Azure AI Search

Docker

Kubernetes

Observability

A/B testing

Memory management

Orchestration patterns

Retrieval-augmented generation

Tools

LangChain

Semantic Kernel

AutoGen

CrewAI

Job description

Job Title/Role: Senior Generative AI Architect

Location: Atlanta, GA

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.