Job Description

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

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

Bachelor’s degree required

M.Tech/ MS in Computer Science, AI, or related field preferred; Required Experience: 8-12 years