Senior AI Engineer, AI Services

Key Responsibilities

Solution Development & Deployment

Build and deploy multi-agent systems using frameworks such as LangChain, LangGraph, Autogen, CrewAI, and LlamaIndex.

Develop custom agents for document processing, workflow automation, SDLC acceleration, data analysis, and business process orchestration.

Integrate LLMs, SLMs, embeddings, and retrieval pipelines (Pinecone, Elasticsearch, Snowflake Cortex, pgvector)

Create and operate LLM/ML endpoints, agent memory/state stores, and event-driven triggers.

Implement reusable components that become part of AHEAD’s agent library and client solution accelerators

Enterprise Integration & Workflow Automation

Build real-time and batch workflows using Python, Kafka, EventBridge, Airflow, Snowflake, S3, n8n, AWS Batch, and similar tools.

Connect agents to enterprise systems (SharePoint, Salesforce, ServiceNow, Jira, Oracle, databases, APIs).

Implement RAG, tool-calling, function calling, and structured output pipelines for production-ready agentic tasks.

Ensure robust data transformations, validation, and versioning for downstream agent workflows.

Quality, Observability & Reliability

Implement monitoring, metrics, and guardrails for multi-agent systems (timeouts, retries, constraints, circuit breakers).

Build automated testing for agent behaviors, prompts, ETL/batch jobs, and model outputs.

Participate in incident reviews, debugging multi-agent flows, and ensuring predictable performance.

Client Collaboration & Delivery Excellence

Work closely with client stakeholders to understand use cases, pain points, and success criteria.

Translate business needs into technical agent designs and execution roadmaps.

Participate in agile ceremonies, demos, and working sessions with client teams.

Contribute to proposals, SOWs, architecture diagrams, and client documentation when needed.

Security, Governance & Compliance

Apply enterprise standards for data security, access control, auditing, model governance, and safe AI usage.

Embed monitoring, lineage, PII handling, and policy constraints into agentic flows.

Mentorship & Internal Development

Coach junior engineers on agent design patterns, RAG, orchestration, and clean engineering practices.

Contribute to internal best practices, reference architectures, and reusable components.

Support onboarding of new engineers and help scale AHEAD's agentic engineering community.

Qualifications

Required

Strong Python engineering background, including async patterns, APIs, and event-driven design.

Hands-on experience with multi-agent frameworks (LangGraph, Autogen, CrewAI, LangChain, etc.).

Demonstrated ability to build production ETL, orchestration, or workflow automation pipelines (Kafka, EventBridge, Airflow, Celery, n8n, AWS services).

Experience with vector DBs and retrieval pipelines (Pinecone, pgvector, Elasticsearch, LlamaIndex).

Familiarity with MLOps, observability, CI/CD, containerization, and model deployment patterns.

Strong documentation habits and comfort working in fast-paced agile environments.

Experience integrating with enterprise systems or APIs in production.

Preferred

Experience with Snowflake Cortex, Databricks Mosaic, NVIDIA NIMs, or similar AI platform components.

Experience operating agentic systems at scale, including safety constraints and system-level debugging.

Experience in consulting, client-facing engineering, or co-development models.

Success Metrics & Environment

Delivery of reliable, scalable agentic solutions that measurably improve client outcomes.

High client satisfaction, repeat demand, and strong cross-functional collaboration.

Consistent contribution to reusable accelerators and internal knowledge base.

Predictable delivery cadence with strong engineering quality and observability.

Visible growth of AHEAD's reputation for agentic AI expertise.