Infopact, Inc.
Senior MLOps / AI Platform Engineer
- Compensation
- Not disclosed
- Location
- Arlington, VA
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 9+ years
- Category
- Devops
- Posted
Skills
Nice to have
- Experience deploying applications within the War Data Platform, formerly Advana, or integrating with WDP-hosted Databricks services.
- Experience supporting AWS GovCloud, DoD IL4/IL5, CUI, or other highly regulated environments.
- Experience with Databricks Genie, Unity Catalog, Databricks Vector Search/AI Search, and user-level OAuth integrations.
- Experience integrating reusable chatbot widgets or APIs into multiple websites, such as PBIS, Jupiter Homepage, Community Portal, or similar enterprise applications.
- Experience with Terraform, CloudFormation, AWS CDK, Helm, Kubernetes, and Git-based CI/CD platforms.
Full description
Job Description:
Build, integrate, deploy, and operate the AWS-hosted chatbot and orchestration services, including its connections to Databricks, enterprise documents, vector stores, and participating websites.
- 9+ years of experience in MLOps, cloud engineering, platform engineering, DevSecOps, or production AI application development.
- Hands-on experience building and deploying AI/ML applications using the AWS technology stack, including Amazon Bedrock and/or SageMaker, API Gateway, ECS or EKS, ECR, S3, IAM, Secrets Manager, KMS, CloudWatch, and related services.
- Strong experience containerizing Python-based applications and APIs using Docker and deploying them into scalable ECS, EKS, or Kubernetes environments.
- Hands-on experience integrating applications with Databricks REST APIs, preferably including the Genie Conversation API, SQL Statement Execution API, SQL Warehouses, OAuth authentication, and Unity Catalog-governed data.
- Experience building API-based chatbot or agent-orchestration services using Python and frameworks such as FastAPI, Flask, LangChain, LangGraph, or comparable technologies.
- Experience implementing routing across structured-data, RAG, and hybrid question-answering workflows.
- Experience connecting AI applications to vector stores such as Amazon OpenSearch Serverless, Bedrock Knowledge Bases, PostgreSQL/pgvector, or Databricks Vector Search.
- Experience developing document-ingestion pipelines for SharePoint, S3, file repositories, or other enterprise knowledge sources, including extraction, chunking, embeddings, metadata enrichment, synchronization, and deletion handling.
- Experience implementing secure token handling, OAuth/OIDC flows, service-principal authentication, secrets management, least-privilege IAM, and server-side session management.
- Experience creating CI/CD pipelines for containerized AI applications, including automated testing, vulnerability scanning, image promotion, deployment, rollback, and configuration management.
- Experience load-testing and scaling chatbot or model-enabled applications based on concurrent users, request volume, model latency, token utilization, and downstream API constraints.
- Experience implementing production observability, including application logging, distributed correlation IDs, metrics, tracing, API latency monitoring, model usage monitoring, alerting, and audit-log integration.
- Ability to troubleshoot issues spanning application code, Docker containers, AWS networking, IAM, Databricks APIs, SQL execution, model endpoints, and vector retrieval.
- Ability to implement guardrails that limit data returned to the model, restrict the application to approved Databricks views, prevent credentials from reaching the browser, and preserve end-to-end auditability.