AI / ML Forward Deployed Engineer

Sehgal Distributors Panchkula; Hyderabad; On-site; INR 2,500,000 - 5,000,000; Full time

Job summary

Sehgal Distributors Panchkula seeks an AI / ML Forward Deployed Engineer to embed with enterprise clients and translate complex data architectures and LLMs into reliable business solutions.

The role focuses on building production-grade AI pipelines, integrating with legacy data environments, and driving adoption with measurable business impact across client sites.

Qualifications

• 35+ years of professional experience as a Software Engineer, Data Engineer, or ML Engineer in a production environment.
• Experience deploying AI applications in enterprise settings and client-facing projects.
• Strong coding fundamentals with Python; familiarity with TypeScript is valued.

Responsibilities

• Embed with client teams to deploy tailored AI solutions in their infrastructure.
• Build production-grade ML pipelines, including RAG systems and multi-agent workflows.
• Integrate AI platforms with complex data environments and cloud architectures.
• Create observability, evaluation benchmarks, and continuous monitoring for AI systems.
• Translate business goals into concrete technical milestones for stakeholders.
• Drive adoption, measure outcomes, and scale systems across environments.

Skills

Python

TypeScript

Communication

Education

Bachelors in Computer Science, Data Science, Mathematics

Tools

PyTorch

LangChain

LlamaIndex

Hugging Face

Vector databases (Pinecone, Milvus, Qdrant)

Prompt engineering frameworks

AWS/Azure/GCP

Docker/Kubernetes

Airflow/Spark

Job description

Job Title: AI / ML Forward Deployed Engineer (FDE)

Location: Hyderabad

Reports To: CTO

About the Role:

We are looking for a highly skilled and adaptable AI / ML Forward Deployed Engineer (FDE) to bridge the gap between our cutting-edge AI technologies and real-world enterprise applications. In this role, you will not be working in an isolated research lab; instead, you will embed directly with our strategic enterprise clients to build, customize,

and scale AI production systems.

As the technical face of our company, you will own the "last-mile gap"turning complex data architectures, Large Language Models (LLMs), and machine learning frameworks into reliable, highly adopted business solutions that drive immediate value.

What You Will Do: (Responsibilities)

• Embed on the Front Lines: Partner directly with client engineering and business teams to understand their operations and deploy tailored AI solutions within their infrastructure.
• Build End-to-End AI Pipelines: Architect and implement production-grade machine learning workflows, including Retrieval-Augmented Generation (RAG) systems, multi-agent frameworks, fine-tuning, and vector database integrations.
• Bridge Legacy Systems & Modern AI: Seamlessly integrate our AI platforms with complex, messy, or legacy enterprise data environments and cloud architectures.
• Own System Observability & Evaluation: Tackle the non-deterministic nature of generative AI by creating robust evaluation benchmarks, continuous monitoring pipelines, and prompt-tuning workflows.
• Translate & Consult: Serve as the technical translator between executive stakeholders (C-suite) and backend engineering teams, converting high-level business goals into concrete technical milestones.
• Drive Adoption and Impact: Take full accountability for business outcomes, user adoption rates, and system scaling rather than just ticking off delivery checkboxes.

What We Are Looking For: (Requirements)

• Experience: 35+ years of professional experience as a Software Engineer, Data Engineer, or ML Engineer in a production environment (client-facing or consulting experience is a massive plus).
• Strong Coding Fundamentals: Expert proficiency in Python is required. Familiarity with languages like TypeScript is highly valued.
• AI/ML & GenAI Stack: Hands-on experience building with modern AI tools such as PyTorch, LangChain, LlamaIndex, Hugging Face, vector databases (e.g.,Pinecone, Milvus, Qdrant), and prompt engineering frameworks.
• Cloud & DevOps Architecture: Solid experience with cloud infrastructure (AWS, Azure, or GCP) along with containerization and orchestration tools like Docker and Kubernetes.
• Enterprise Data Proficiency: Strong data modeling skills, including experience with SQL/NoSQL databases, data pipelines (Airflow, Spark), and ETL processing.
• Communication & Empathy: Exceptional emotional intelligence (EQ) and communication skills. You can explain complex AI concepts to non-technical stakeholders and maintain composure in high-stakes environments.
• Travel: Willingness to travel up to [X%] to client sites as required. Nice-to-Haves
• A degree in Computer Science, Data Science, Mathematics, or a related quantitative field.
• Experience deploying AI applications in highly regulated industries (e.g., Finance, Healthcare, Government) with strict security/privacy requirements.
• Contributions to open-source AI/ML projects or frameworks.