Lead architecture and delivery of enterprise Generative AI solutions: design LLM-based RAG pipelines, multi-agent systems, and secure cloud deployments; establish LLMOps best practices; advise clients, run PoCs, and mentor engineering teams to operationalize scalable, compliant AI systems.
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
ResponsibilitiesResponsibilities
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
QualificationsBachelor’s degree required
M.Tech/ MS in Computer Science, AI, or related field preferred; Required Experience: 8-12 years
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Delhi, India's capital city, is a place where tradition and progress co-exist. While Old Delhi is known for its rich history and bustling markets, New Delhi is defined by its modern architecture. It's clear the region places a strong emphasis on preserving its cultural heritage while embracing technological advancements, particularly in artificial intelligence, which plays a central role in shaping the city's tech landscape, fueled by investments in research and development.
