AI Forward Deployed Engineer

Role & responsibilities

Title: AI Forward Deployed Engineer

Duration 6-month renewals starting in 2027

Location: Fully Remote (India)

Work Hours: Candidates are expected to support upto 12 Noon ET

• MarchNovember: 12:30 PM – 9:30 PM IST
• November–March: 1:30 PM – 10:30 PM IST

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

Software Engineering

• 3+ years of experience in full stack development with strong Python programming skills
  • Solid software engineering practices: design patterns, SOLID principles, clean code, code reviews
  • Experience with automated testing (pytest, unittest, integration tests, E2E tests)
  • Proficiency in Git, branching strategies, and release management
  • Working proficiency in TypeScript for front-end/UI development, sufficient to build and connect user interfaces alongside Python back-end services
  • Knowledge of relational and NoSQL databases (PostgreSQL, MongoDB, Redis, or similar)

AI and Machine Learning

• Experience with LLM frameworks (LangChain, LlamaIndex) and Hugging Face
  • Practical knowledge of vector databases (Qdrant, Milvus, Pinecone, ElasticSearch)
  • Experience with chunking strategies, embedding selection/tuning, and rerankers
  • Familiarity with complex document processing: table extraction, layout parsing, OCR, and multimodal LLM-based image description

Soft Skills and Business Engagement

• 3+ years of experience leading customer/client-facing workshops and discussions
  • Demonstrated business analysis skills and ability to translate requirements into technical solutions
  • Excellent technical communication and documentation skills

Preferred candidate profile

Perks and benefits