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AVP/AI Engineer

AllianceBernstein
Pune, IND
Full-time
Executive · 5+ years experience
Onsite
Discovered 1 weeks ago
awsazuredatadogdockergcpgemini

Full Job Posting

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.
  • 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

Qualifications

  • 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.
  • 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
  • · 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.
  • · 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
  • · 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
  • · 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.
  • · 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.
  • · 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.
  • 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
  • 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

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