AVP/AI Engineer
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Before you apply
LocationPune, India
SeniorityVP
Experience5+ yrs
DepartmentData Engineering
Company size10,000+ people
PostedOct 14, 2026 · 3w ago
Verified live4d ago
At a glanceSummarised by Seekless from the posting.
5+ years of experience in software, data, AI, or platform engineering
Strong production development experience with Python and SQL
Proven ability to build and operate AI applications, data pipelines, APIs, data products, and distributed systems
Experience integrating AI capabilities into enterprise applications and business workflows
Strong understanding of object-oriented and asynchronous Python
Strong understanding of APIs, microservices, and application integration
Strong understanding of data modeling, contracts, quality, reconciliation, metadata, and lineage
Strong understanding of batch, streaming, event-driven, and API-based architectures
Strong understanding of workflow orchestration, distributed processing, and production operations
Experience with cloud data and AI platforms such as Azure, AWS, Google Cloud, Snowflake
Ability to design scalable, reusable, resilient, and operationally supportable services
Experience with large language models, embeddings, prompt engineering, context engineering, and RAG architecture
Experience with commercial and open-source model APIs and ecosystems
Experience with AI agents, agentic workflows, reusable skills, tool calling, structured outputs, and multi-step orchestration
Experience with vector, keyword, metadata-filtered, graph-based, and hybrid retrieval
Experience with agent frameworks such as LangGraph, Microsoft Agent Framework, Semantic Kernel, AutoGen, Pydantic AI, or equivalent platforms
Hands-on experience with data-quality, model-evaluation, LLM-evaluation, or AI-observability frameworks
Ability to independently validate AI-generated code for functional correctness and test coverage
Ability to independently validate AI-generated code for security and data-access risks
Ability to independently validate AI-generated code for performance and scalability
Ability to independently validate AI-generated code for maintainability and architectural alignment
Ability to independently validate AI-generated code for dependency, licensing, and software supply-chain concerns
Ability to independently validate AI-generated code for compliance with enterprise engineering standards
Strong experience with CI/CD, infrastructure as code, automated provisioning, and governed deployment
Strong experience with API-first and microservices architectures
Strong experience with Docker, Kubernetes, AKS, GKE, or equivalent container platforms
Strong experience with authentication, authorization, secrets management, OAuth, managed identities, RBAC, and Microsoft Entra ID or equivalent technologies
Strong experience with repository governance, branch protection, pull-request validation, dependency controls, and software supply-chain security
Strong experience with metrics, distributed tracing, structured logging, monitoring, and alerting
Strong experience with designing observable, resilient, secure, and production-ready AI systems
MCP servers and clients, agent-to-agent communication, and enterprise search
Semantic layers, ontologies, and knowledge graphs
Agent memory, state management, and human-in-the-loop controls
Document intelligence and multimodal processing
Reusable agent tools, skills, manifests, and registration standards
Skills
Python
SQL
Azure
AWS
Google Cloud
Snowflake
LangGraph
Microsoft Agent Framework
Semantic Kernel
AutoGen
Pydantic AI
Azure OpenAI
Anthropic Claude
Google Gemini
Hugging Face
vLLM
Ollama
Qwen
Docker
Kubernetes
AKS
GKE
Claude Code
GitHub Copilot
Cursor
Codex
OpenTelemetry
OpenInference
Datadog
Arize
LangSmith
MLflow
Company Description
As a leading global investment management firm, AB fosters diverse perspectives and embraces innovation to help our clients navigate the uncertainty of capital markets. Through high-quality research and diversified investment services, we serve institutions, individuals, and private wealth clients in major markets worldwide. Our ambition is simple: to be our clients’ most valued asset-management partner.
With over 4,400 employees across 51 locations in 25 countries, our people are our advantage. We foster a culture of intellectual curiosity and collaboration to create an environment where everyone can thrive and do their best work. Whether you’re producing thought-provoking research, identifying compelling investment opportunities, infusing new technologies into our business, or providing thoughtful advice to clients, we’re looking for unique voices to help lead us forward. If you’re ready to challenge your limits and build your future, join us.
About the role
Who You’ll Work With:
Data Engineering team under Center of Excellence (Data Science)
What You’ll Do:
AI and Data Engineer, Agentic AI Platform
We are building a next-generation enterprise AI and data platform that enables teams to design, deploy, integrate, and scale intelligent applications and agents across investment research, portfolio management, risk, client, and operational workflows.
The platform provides standardized infrastructure, reliable data products, commercial and self-hosted model access, retrieval, agent orchestration, evaluation, observability, security, and governance. It enables teams to rapidly deliver AI-powered capabilities in a scalable, secure, and production-ready manner.
We are looking for hands-on AI and Data Engineers who can:
•
Build reliable, reusable, and AI-ready data products for investment and enterprise use cases.
•
Design and develop production-grade AI applications, agents, and intelligent workflows using Python.
•
Implement agent orchestration, tool integration, retrieval, memory, and multi-step workflows.
•
Integrate AI capabilities into enterprise applications, data platforms, and business systems.
•
Contribute to the shared AI and data platform that powers these capabilities at scale.
This role operates at the intersection of AI engineering, data engineering, agentic AI, platform engineering, and investment technology. It emphasizes Python, enterprise integration, reliable data products, security, CI/CD, observability, model and agent evaluation, and rigorous validation of production AI systems.
Requirements
What We’re Looking For:
Core AI and Data Engineering
· 5+ years of experience in software, data, AI, or platform engineering.
· Strong production development experience with Python and SQL.
· Proven ability to build and operate AI applications, data pipelines, APIs, data products, and distributed systems.
· Experience integrating AI capabilities into enterprise applications and business workflows.
· Strong understanding of:
o Object-oriented and asynchronous Python
o APIs, microservices, and application integration
o Data modeling, contracts, quality, reconciliation, metadata, and lineage
o Batch, streaming, event-driven, and API-based architectures
o Workflow orchestration, distributed processing, and production operations
· Experience with cloud data and AI platforms such as Azure, AWS, Google Cloud, Snowflake.
· Ability to design scalable, reusable, resilient, and operationally supportable services.
Generative AI and Agentic Systems
Hands-on experience with:
· Large language models, embeddings, prompt engineering, context engineering, and RAG architecture.
· Commercial and open-source model APIs and ecosystems.
· AI agents, agentic workflows, reusable skills, tool calling, structured outputs, and multi-step orchestration.
· Vector, keyword, metadata-filtered, graph-based, and hybrid retrieval.
· Agent frameworks such as LangGraph, Microsoft Agent Framework, Semantic Kernel, AutoGen, Pydantic AI, or equivalent platforms.
Preferred experience includes:
· MCP servers and clients, agent-to-agent communication, and enterprise search.
· Semantic layers, ontologies, and knowledge graphs.
· Agent memory, state management, and human-in-the-loop controls.
· Document intelligence and multimodal processing.
· Reusable agent tools, skills, manifests, and registration standards.
Model Hosting and Inference
Experience with one or more of the following:
· Azure OpenAI, OpenAI APIs, Anthropic Claude, Google Gemini, or Vertex AI.
· Hugging Face, vLLM, Ollama, Qwen, or other approved open-source model technologies.
· Containerized model deployment and GPU-based inference.
· Model serving, API gateways, quantization, memory optimization, and model configuration.
· Context-window, token, concurrency, batching, throughput, and caching optimization.
· Benchmarking and monitoring model quality, latency, availability, resource utilization, and cost.
Platform Engineering, Security, and DevOps
Strong experience with:
· CI/CD, infrastructure as code, automated provisioning, and governed deployment.
· API-first and microservices architectures.
· Docker, Kubernetes, AKS, GKE, or equivalent container platforms.
· Authentication, authorization, secrets management, OAuth, managed identities, RBAC, and Microsoft Entra ID or equivalent technologies.
· Repository governance, branch protection, pull-request validation, dependency controls, and software supply-chain security.
· Metrics, distributed tracing, structured logging, monitoring, and alerting.
· Designing observable, resilient, secure, and production-ready AI systems.
Experience using AI-assisted development tools such as Claude Code, GitHub Copilot, Cursor, Codex, or comparable tools for:
· Code generation, refactoring, debugging, testing, and documentation.
· Data-pipeline, infrastructure, and CI/CD development.
· Codebase understanding, modernization, architecture extraction, and pull-request review.
· Governed development supported by automated testing, peer review, security scanning, and controlled deployment.
Candidates must be able to independently validate AI-generated code for:
· Functional correctness and test coverage
· Security and data-access risks
· Performance and scalability
· Maintainability and architectural alignment
· Dependency, licensing, and software supply-chain concerns
· Compliance with enterprise engineering standards
Evaluation, Observability, and Testing: Required
· Hands-on experience with data-quality, model-evaluation, LLM-evaluation, or AI-observability frameworks.
· Experience building:
o Model benchmarking and evaluation pipelines
o Data-quality and reconciliation tests
o Retrieval and RAG evaluations
o Agent test harnesses and scenario-based simulations
o Continuous validation and regression testing
o Production monitoring for data, retrieval, model, and agent quality
· Ability to measure:
o Data accuracy, completeness, freshness, and consistency
o Retrieval relevance and coverage
o Groundedness, factual consistency, citations, and provenance
o Tool selection, execution, and structured-output compliance
o Agent workflow success, failure, and escalation behavior
o Latency, throughput, reliability, resource utilization, and cost
· Familiarity with OpenTelemetry, OpenInference, Datadog, Arize, LangSmith, MLflow, Microsoft Foundry evaluation capabilities, or equivalent tools.
· Ability to use evaluation results to inform model selection, routing, release, rollback, and production-readiness decisions.
Location: Pune, India
Pune, India