MSR Technology Group
Website:
msrtechnologies.com
Job details:
Title: AI Forward Deployed Engineer
Location: Remote
Duration: 6+ months extendable
Overview:
• Build and deploy production AI solutions across AWS, APIs, CI/CD, monitoring, integrations, and enterprise platforms
• Strong platform/application engineering background with hands-on experience operationalizing LLM, agentic AI, and cloud-based solutions
• Experience with AWS Bedrock, LangChain, Snowflake, Lambda, enterprise security/governance, and cross-functional collaboration with data and infrastructure teams.
Key Responsibilities:
Infrastructure and Cloud Engineering
• Cloud Architecture: Design, implement, and manage scalable and secure infrastructure on cloud platforms (AWS/Azure) for production AI applications
• Containerization and Orchestration: Develop and maintain containerized environments (Docker) for model and application deployment
• CI/CD Pipelines: Build and optimize continuous integration and delivery pipelines for automated testing, builds, and deployments
Software Engineering and Productionization
• Full Stack Development: Build robust APIs (FastAPI, Flask) and user interfaces for AI applications, ensuring end-to-end integration
• Quality and Testing: Implement comprehensive testing strategies (unit, integration, E2E) and code reviews to ensure code quality
• Version Control and Code Management: Establish Git flow practices, branching strategies, and release management
• Technical Documentation: Create and maintain architecture documentation, runbooks, deployment guides, and operational procedures
Monitoring, Observability, and Performance
• Observability: Implement logging, tracing, and metrics solutions (Prometheus, Grafana, ELK Stack, CloudWatch, Application Insights)
• Performance and Optimization: Monitor and optimize latency, throughput, resource utilization, and operational costs
• Alerting and Incident Response: Configure proactive alerting systems and establish incident response procedures
AI Applications and RAG
• Conversational Agents: Implement LLM agents with function calling, state management, guardrails, and safety mechanisms
• RAG Pipelines: Build indexing, chunking, embeddings, reranking, and hybrid retrieval pipelines for high-precision answers
• Document AI: Process and parse complex PDFs using OCR, multimodal LLM image understanding, table extraction, and layout-aware parsing
• Prompt Engineering: Design and iterate prompts, templates, and few-shot examples; run A/B and automated evaluations
Project Management and Communication
• Project Documentation: Create and maintain written project documentation including requirements, technical designs, test plans, runbooks, release notes, user guides, risk/issue logs, and change records
• Stakeholder Communication: Produce status updates, meeting notes, incident/outage notices, and decision summaries
• Workshops and Discussions: Lead customer/client-facing workshops and discussions
Skills:
Cloud and Infrastructure
• 3+ years of experience designing and implementing cloud infrastructure (AWS/Azure) for production applications
• Practical expertise in containerization (Docker)
• Knowledge of Infrastructure as Code (Terraform, CloudFormation, or similar)
• Solid experience with CI/CD (GitHub Actions, GitLab CI, Jenkins, Azure DevOps) and deployment automation
• Experience with cloud networking, security, IAM, and secrets management (Vault, AWS Secrets Manager, Azure Key Vault)
• Hands-on experience building data pipelines on cloud and working with cloud data sources and services (S3, AWS OpenSearch, data lakes); not a dedicated data engineer role, but comfort with data-centric cloud services is required
EoE
Click on Apply to know more.
