AVP/AI Engineer

Overview

Build and operate the enterprise AI and data platform for AllianceBernstein's Data Engineering team under the Center of Excellence (Data Science), focused on production-grade AI applications, agents and data products.

What you'll do

• 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.

What you'll need

• 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 or Snowflake.
• Ability to design scalable, reusable, resilient and operationally supportable services.
• Hands-on experience with large language models, embeddings, prompt engineering, context engineering and RAG architecture.
• Hands-on experience with commercial and open-source model APIs and ecosystems.
• Hands-on experience with AI agents, agentic workflows, reusable skills, tool calling, structured outputs and multi-step orchestration.
• Hands-on experience with vector, keyword, metadata-filtered, graph-based and hybrid retrieval.
• Hands-on experience with agent frameworks such as LangGraph, Microsoft Agent Framework, Semantic Kernel, AutoGen, Pydantic AI or equivalent platforms.
• Experience with one or more of: Azure OpenAI, OpenAI APIs, Anthropic Claude, Google Gemini or Vertex AI.
• Experience with one or more of: Hugging Face, vLLM, Ollama, Qwen or other approved open-source model technologies.
• Experience with containerized model deployment and GPU-based inference.
• Experience with model serving, API gateways, quantization, memory optimization and model configuration.
• Experience with context-window, token, concurrency, batching, throughput and caching optimization.
• Experience benchmarking and monitoring model quality, latency, availability, resource utilization and cost.
• 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 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.
• Ability to independently validate AI-generated code for functional correctness, test coverage, security and data-access risks, performance, scalability, maintainability, architectural alignment, dependency and licensing concerns, and compliance with enterprise engineering standards.
• Required: hands-on experience with data-quality, model-evaluation, LLM-evaluation or AI-observability frameworks.
• Required: experience building model benchmarking and evaluation pipelines, data-quality and reconciliation tests, retrieval and RAG evaluations, agent test harnesses and scenario-based simulations, continuous validation and regression testing, and production monitoring for data, retrieval, model and agent quality.
• Required: ability to measure data accuracy, completeness, freshness and consistency; retrieval relevance and coverage; groundedness, factual consistency, citations and provenance; tool selection, execution and structured-output compliance; agent workflow success, failure and escalation behavior; and latency, throughput, reliability, resource utilization and cost.
• Required: ability to use evaluation results to inform model selection, routing, release, rollback and production-readiness decisions.

Nice to have

• 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.
• Familiarity with OpenTelemetry, OpenInference, Datadog, Arize, LangSmith, MLflow, Microsoft Foundry evaluation capabilities or equivalent tools.

Details

• Location: Pune, India.