Forward Deployed Engineer Fde Orcapod Consulting Services Chennai
Explicitly uses AI-assisted development tools (Copilot, Cursor, Replit) and focuses on prompt/context engineering and MLOps—so tight connection to vibe coding and AI-driven prototyping.
About the Role
Lead implementation of AI-driven development practices across product and engineering teams by operationalizing AI-DLC, AI-assisted development, and MLOps/LLMOps toolchains. Establish reference architectures, coding standards, semantic assets, and coach teams to deliver cloud-native, AI-integrated enterprise applications.
Job Description
Role
Forward Deployed Engineer responsible for operationalizing AI-driven development lifecycle (AI-DLC) across cross-functional teams. The role focuses on enabling AI-assisted development, prompt and context engineering, semantic modeling, and introducing MLOps/LLMOps practices to support delivery in brownfield and enterprise environments.
Key Responsibilities
- Work hands-on with Product, Architecture, Engineering, QA, and UX teams to implement AI-DLC practices.
- Enable mob construction and AI-assisted development workflows (prompt engineering, context-driven delivery).
- Establish reference architectures, AI coding standards, reusable prompts, and semantic knowledge assets.
- Coach teams on validation frameworks, AI quality practices, and human-in-the-loop controls.
- Support semantic modeling, knowledge extraction from legacy systems, and RAG/semantic search patterns.
- Introduce AI engineering toolchains, evaluation pipelines, and MLOps/LLMOps practices.
- Capture delivery learnings and convert them into reusable best practices.
Requirements
- Bachelor’s degree in Computer Science, Engineering, or related field.
- 8+ years of experience in software engineering, architecture, QA, DevOps, or platform engineering.
- Hands-on experience delivering cloud-native and enterprise applications.
- Experience with AI-assisted development tools and modern software engineering practices.
- Strong coding and solution architecture background.
Preferred
- Experience with LLMs, Agentic AI, RAG, or semantic search.
- AWS, Azure, or GCP certifications.
- Experience in AI platform engineering and MLOps/LLMOps.
- Financial services domain experience.
Technical & Soft Skills
- Prompt engineering and context engineering.
- Programming: Python, Java, JavaScript or other modern cloud development stacks.
- DevOps/CI/CD practices and tools.
- Familiarity with GenAI platforms and LLM integration.
- Semantic modeling and knowledge graph concepts.
- Coaching, mentoring, and strong verbal/written communication.