Forward Deployed Engineer Fde Orcapod Consulting Services Chennai

Vibehackers; Chennai District; On-site; INR 400,000 - 700,000; Full time

Job summary

Orcapod Consulting Services Chennai is seeking a Forward Deployed Engineer to operationalize AI-driven development across product and engineering teams in enterprise environments. You will implement AI-DLC, AI-assisted development, and MLOps/LLMOps toolchains, and establish reference architectures and coding standards.

You will coach teams on validation, semantic modeling, and knowledge extraction from legacy systems, enabling cloud-native AI-integrated applications and guiding brownfield

Qualifications

• Bachelor's degree in CS/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.

Responsibilities

• Work hands-on with Product, Architecture, Engineering, QA, and UX teams to implement AI-DLC practices.
• 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.

Skills

Prompt Engineering

Context Engineering

Solution Architecture

Coaching/Mentoring

Collaboration

Communication

DevOps CI/CD

MLOps

LLMOps

Semantic Modeling

Knowledge Graphs

Quality Assurance

Platform Engineering

Software Engineering

Education

Bachelor's degree in Computer Science, Engineering, or related field

Job description

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.
• 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.

Technical & Soft Skills

• Prompt engineering and context engineering.
• 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.

Skills

Prompt Engineering Context Engineering Solution Architecture Coaching Mentoring Collaboration Communication DevOps CI/CD MLOps LLMOps Semantic Modeling Knowledge Graphs Quality Assurance Platform Engineering Software Engineering