Responsibilities

AI Architecture & Solution Design

• Architect enterprise-grade GenAI solutions using LLMs, embeddings, and vector databases. • Design scalable RAG pipelines and knowledge-grounded AI systems. • Define agentic workflows with reasoning, tool usage, and memory capabilities. • Establish secure, compliant AI deployment architectures across cloud platforms.

Agentic AI & Automation

• Design multi-agent systems for workflow automation and decision intelligence. • Implement orchestration logic, tool integration layers, and human-in-the-loop controls. • Define evaluation, guardrails, and monitoring frameworks for agent performance.

AI Platform Management & Operational Excellence

• Establish standards and best practices for LLMOps / MLOps, covering the full model lifecycle from development to production.

• Assess and select foundation models (OpenAI, open-source LLMs) for suitability, performance, and compliance in enterprise contexts. • Ensure AI solution efficiency and robustness by optimizing cost, latency, scalability, and system reliability.

Client Advisory & Pre-Sales Support

• Act as AI solution architect in client discussions and transformation initiatives. • Lead PoCs, technical demonstrations, and innovation workshops. • Translate business objectives into scalable AI system designs.

Innovation & Enablement

• Stay current with evolving GenAI and agent frameworks. • Develop architectural playbooks, reference patterns, and reusable accelerators. • Mentor engineering teams on best practices in AI system design.

Experience and Competency Requirements

• 8-12 years of experience in AI/ML engineering and architecture. • Minimum 2-3 years hands-on experience with Generative AI systems. • Strong expertise in LLMs, RAG architectures, embeddings, and vector stores. • Experience designing and deploying production-grade AI applications. • Hands-on experience with cloud-native AI deployments (AWS / Azure / GCP). • Strong problem-solving and client-facing communication skills. • Ability to operate in a consulting or managed services environment. • Should have decent to good experience in data handling and analytics with python

Nice to have capabilities

• Previous experience in pre-sales & consulting is preferred. • Experience leading enterprise AI transformation initiatives. • Exposure to industry-specific AI applications (Insurance, Healthcare, Banking, Media). • Experience integrating AI into large-scale operational workflows.

Skills

GenAI & LLM Frameworks (Mandatory)

• OpenAI APIs / Azure OpenAI • LangChain / LangGraph / LlamaIndex • Transformers (Hugging Face) • Prompt engineering and evaluation frameworks

Agentic Systems & Orchestration

• Multi-agent design patterns (MCP, A2A, ReAct etc) • Tool integrations and API orchestration • Memory frameworks and contextual reasoning • Guardrails, observability, and monitoring

Data & Infrastructure

• Vector databases (Pinecone, FAISS, Weaviate or equivalent) • Python, FastAPI, REST services • Docker, Kubernetes • Cloud platforms (AWS, Azure, GCP)

Data Handling & Analytics Skills

• Data preprocessing and ETL for structured and unstructured data • Data manipulation using Pandas, NumPy, and SQL • Exploratory data analysis (EDA) and statistical analysis • Data visualization (Matplotlib, Seaborn, Plotly, Tableau, Power BI) • Metrics design for AI evaluation, monitoring, and performance measurement • Knowledge of data quality, validation, and governance best practices

Advanced Capabilities

• Fine-tuning and model evaluation • AI governance and responsible AI

Cost optimization and performance benchmarking