AI Solutions Engineer

Dautom; India; Remote; INR 3,500,000 - 6,000,000; Full time

Job summary

Dautom is seeking an hands-on AI Solutions Engineer in India to lead end-to-end generative AI initiatives for a global client. You will own LLM orchestration, retrieval, backend services, and frontend interfaces, delivering production-grade AI workloads with an emphasis on reliability, security, and cost efficiency.

You will collaborate across infra, data, and product teams, mentoring engineers and shipping reusable components while optimizing latency, throughput, and developer experience in a

Qualifications

• 4 to 10 years of software or ML engineering experience, including at least 2 years building LLM or generative AI applications
• A proven track record of shipping at least one GenAI or agentic application to production with real users
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
• Strong system design skills, with the ability to reason about reliability, security, and cost trade-offs

Responsibilities

• Architect and build multi-agent systems with stateful orchestration, tool calling, routing, memory, and human-in-the-loop checkpoints
• Design retrieval pipelines covering document parsing, chunking, embeddings, hybrid search, and reranking, grounded in governed enterprise data
• Build high-performance Python backend services and streaming APIs that expose agents to web, chat, and voice channels
• Develop responsive React and TypeScript front ends with real-time streaming, rich visualizations, and clean UX for business users
• Deploy and operate AI workloads on the lakehouse platform and cloud, using containers, CI/CD, and infrastructure as code
• Implement LLMOps practices: offline and online evaluation, LLM-as-judge scoring, tracing, prompt versioning, and regression testing
• Engineer guardrails for prompt-injection defense, PII protection, output validation, and role-based data access
• Optimize latency, token cost, and throughput through model selection, caching, prompt design, and parallel execution
• Translate business requirements into technical designs, and document architecture decisions for review
• Mentor engineers and contribute reusable components, templates, and standards to the wider team

Skills

Python

TypeScript

SQL

LLMs

GenAI

LangGraph

LangChain

LlamaIndex

OpenAI Agents

Vector Databases

FastAPI

REST

WebSockets

OAuth2

React

Databricks

Azure

Docker

CI/CD

Kubernetes

Terraform

Education

Bachelor's or Master's in CS/Engineering

Tools

Databricks

Azure

Git

Job description

In this role, you will collaborate closely with one of our esteemed clients—a globally recognized leader in their industry, distinguished by their commitment to excellence, innovation, and delivering exceptional value. As a trusted IT consulting partner, Dautom is supporting their strategic initiatives by connecting them with exceptional talent to drive business growth and transformation.

About the Role

Job Title: AI Solutions Engineer

Job Type: 1year + extendable long term contract

Remote from India

We are looking for a hands-on AI Solutions Engineer who can take generative and agentic AI solutions from design to production. You will own the full stack: LLM orchestration, retrieval, backend services, user interface, deployment, and evaluation. This is an engineering role first. You will write production code every day and be accountable for the reliability, security, and cost of what you ship.

Key Responsibilities

• Architect and build multi-agent systems with stateful orchestration, tool calling, routing, memory, and human-in-the-loop checkpoints
• Design retrieval pipelines covering document parsing, chunking, embeddings, hybrid search, and reranking, grounded in governed enterprise data
• Build high-performance Python backend services and streaming APIs that expose agents to web, chat, and voice channels
• Develop responsive React and TypeScript front ends with real-time streaming, rich visualizations, and clean UX for business users
• Deploy and operate AI workloads on the lakehouse platform and cloud, using containers, CI/CD, and infrastructure as code
• Implement LLMOps practices: offline and online evaluation, LLM-as-judge scoring, tracing, prompt versioning, and regression testing
• Engineer guardrails for prompt-injection defense, PII protection, output validation, and role-based data access
• Optimize latency, token cost, and throughput through model selection, caching, prompt design, and parallel execution
• Translate business requirements into technical designs, and document architecture decisions for review
• Mentor engineers and contribute reusable components, templates, and standards to the wider team

Technical Skills Required

• Languages: Expert Python (async, typing, Pydantic, testing); working proficiency in TypeScript and SQL
• LLMs and GenAI: Hands-on with frontier models (Claude, GPT, or equivalent), structured outputs, function and tool calling, context engineering, and prompt design
• Agent frameworks: Production experience with LangGraph (strongly preferred) or comparable frameworks such as LangChain, LlamaIndex, or the OpenAI Agents SDK
• Retrieval: Vector databases and search services (Databricks Vector Search, Azure AI Search, pgvector, or similar), embedding models, hybrid retrieval, and reranking
• Backend: FastAPI, REST and streaming protocols (SSE, WebSockets), async I/O, and OAuth2 or Entra ID authentication
• Frontend: React, TypeScript, state management, and component libraries; able to build streaming chat and dashboard interfaces
• Data platform: Databricks (Model Serving, Unity Catalog, MLflow, Databricks Apps) or an equivalent enterprise data and AI platform
• Cloud and DevOps: Microsoft Azure, Docker, Git, and CI/CD using GitHub Actions or Azure DevOps
• LLMOps: Evaluation frameworks, tracing and observability (MLflow Tracing, LangSmith, or similar), and cost and latency monitoring Preferred
• Model Context Protocol (MCP) and agent-to-agent interoperability patterns
• Databricks Genie, Mosaic AI Agent Framework, or Agent Bricks
• Realtime voice and speech APIs (Azure AI Speech, GPT-Realtime, or similar)
• Multilingual or Arabic NLP
• Integration with enterprise systems such as CRM, ERP, Microsoft Teams, and Power Automate
• Kubernetes, Terraform, or Databricks Asset Bundles

Qualifications

• 4 to 10 years of software or ML engineering experience, including at least 2 years building LLM or generative AI applications
• A proven track record of shipping at least one GenAI or agentic application to production with real users
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
• Strong system design skills, with the ability to reason about reliability, security, and cost trade-offs
• Clear written and verbal communication with both technical and business stakeholders
• Relevant certifications (Databricks Generative AI Engineer, Azure AI Engineer) are a plus