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
- Robust 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

📌 AI Forward Deployed Engineer (India)
🏢 MSR Technology Group
📍 India