AI Systems Engineer

Overview:

Engagement Type

Contract

Key Responsibilities

Design, develop, and deploy agentic AI systems

in Python using LangGraph and LangChain, including supervisor and sub-agent

architectures, tool routing, and human-in-the-loop workflows.

Implement agent memory and context management

strategies, including conversational state, long-term semantic memory,

summarization, and context-window optimization.

Integrate AI agents with internal and

third-party systems using the Model Context Protocol (MCP) and Agent-to-Agent

(A2A) protocols.

Build and optimize RAG pipelines end to end,

covering document ingestion, chunking, embedding, hybrid and semantic

retrieval, re-ranking, and access-control-aware filtering.

Develop text-to-SQL and natural-language

analytics capabilities over large relational schemas, including semantic

catalogs, query validation, and execution guardrails.

Design and implement scalable backend services

and microservices in FastAPI, including RESTful API design, request validation

with Pydantic, dependency injection, authentication and authorization, API

versioning, and rate limiting.

Build real-time streaming interfaces using

WebSockets and Server-Sent Events to support token-level LLM response streaming

and long-running agent executions.

Model, provision, and optimize application data

stores, including relational databases such as PostgreSQL, vector databases for

embedding storage and similarity search, document stores such as MongoDB, and

caching layers such as Redis.

Own database lifecycle work including schema

migrations, ORM usage, connection pooling, transaction management, and query

performance tuning under production load.

Containerize applications with Docker and deploy

to Kubernetes, managing autoscaling, resource allocation, secrets, and

progressive rollout strategies.

Implement and maintain LLM gateway and routing

infrastructure, including multi-provider failover, rate limiting, budget

enforcement, and usage attribution.

Establish observability and evaluation practices

for non-deterministic systems, including distributed tracing, structured

logging, automated evaluations, and cost and latency monitoring.

Develop and maintain prompt engineering

practices, including prompt versioning, regression testing, structured output

enforcement, and mitigation of hallucination and prompt-injection risks.

Diagnose and resolve production incidents across

the full stack, and contribute to runbooks, design documentation, and

post-incident reviews.

Collaborate with product, data engineering, and

business stakeholders to translate requirements into technical designs, and

participate in architecture reviews.

Follow engineering best practices in code

review, automated testing, version control, and CI/CD, and contribute to team

technical standards.

Preferred Qualifications

Experience implementing MCP servers or clients,

or A2A-based agent interoperability.

Familiarity with evaluation frameworks for

agentic systems, such as LangSmith or Ragas.

Experience with workflow orchestration platforms

such as Prefect, Temporal, Airflow, or Dagster.

Familiarity with enterprise identity and access

management, including Okta, Microsoft Entra ID, SSO, SCIM, and OAuth 2.0.

Experience with document processing and

ingestion at scale, including OCR, parsing of unstructured formats, and

multimodal inputs.

Experience mentoring engineers or leading

technical design for a delivery team.Required/Desired Skills

Skill Required/Desired Amount of Experience - Experience implementing MCP servers or clients, or A2A-based agent interoperability. Required - Familiarity with evaluation frameworks for agentic systems, such as LangSmith or Ragas. Required - Experience with workflow orchestration platforms such as Prefect, Temporal, Airflow, or Dagster. Required - Familiarity with enterprise identity and access management, including Okta, Microsoft Entra ID, SSO, SCIM, and OAuth 2.0. Required - Experience with document processing and ingestion at scale, including OCR, parsing of unstructured formats, and multimodal inputs. Required - Experience mentoring engineers or leading technical design for a delivery team. Required

Skills:

- Experience implementing MCP servers or clients,or A2A-based agent interoperability.,- Familiarity with evaluation frameworks for agentic systems,such as LangSmith or Ragas.,- Experience with workflow orchestration platforms such as Prefect,Temporal,Airflow,or Dagster.,- Familiarity with enterprise identity and access management,including Okta,Microsoft Entra ID,SSO,SCIM,and OAuth 2.0.,- Experience with document processing and ingestion at scale,including OCR,parsing of unstructured formats,and multimodal inputs.,- Experience mentoring engineers or leading technical design for a delivery team.