Sr. Gen AI Engineer

IndiaRemoteContractsenior

Machine Learning EngineerData

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Quick Summary

Overview

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Sr. Gen AI Engineer based in India.

Technical Tools

Machine Learning EngineerData

This is a hands-on senior engineering opportunity focused on building and productionizing advanced AI and Generative AI solutions.
You will transform complex and sometimes ambiguous business challenges into practical AI architectures, prototypes, and scalable production systems.
The role combines deep technical engineering with forward deployment, working closely with customers, product teams, and technical stakeholders.
You will develop LLM-powered applications, intelligent workflows, and AI-driven automation while balancing performance, reliability, security, and cost.
The environment is fast-paced and innovation-driven, with opportunities to evaluate emerging models, frameworks, and agentic AI technologies.
You will also contribute reusable APIs, components, evaluation frameworks, and engineering practices that accelerate AI adoption.
This contract role is well suited to an experienced AI engineer who enjoys ownership, experimentation, and delivering measurable real-world solutions.

Design, develop, and implement scalable AI and Generative AI solutions aligned with business and product requirements.
Build and productionize LLM-powered applications, intelligent automation systems, and AI-driven workflows.
Partner directly with customers, product teams, and internal stakeholders to understand real-world challenges and deliver tailored AI solutions.
Translate ambiguous business requirements into practical AI architectures, prototypes, and production-ready implementations.
Develop, test, deploy, and optimize AI models and applications across cloud and enterprise environments.
Integrate foundation models and LLM APIs into scalable applications while considering performance, reliability, security, and cost.
Evaluate emerging Generative AI technologies, frameworks, and models and assess their suitability for specific business use cases.
Troubleshoot model and application issues while continuously improving quality, latency, scalability, and accuracy.
Collaborate with software engineers, data scientists, product managers, and other stakeholders to deliver end-to-end AI solutions.
Develop reusable components, APIs, tools, and frameworks that accelerate the deployment and adoption of AI applications.
Establish effective evaluation, monitoring, testing, and observability practices for AI and LLM-based systems.
Stay current with developments in Generative AI, LLMs, AI agents, model orchestration, and enterprise AI deployment.

Requirements

~2 min read

5–13 years of professional experience in software engineering, Artificial Intelligence, Machine Learning, or related technical fields.
Strong hands-on experience developing AI and Generative AI applications.
Solid understanding of Large Language Models, foundation models, prompt engineering, model APIs, and AI application architecture.
Proven ability to take AI solutions from experimentation and prototyping through production deployment.
Strong forward deployment engineering mindset, with the ability to work directly with users or customers and rapidly develop solutions around their requirements.
Strong programming fundamentals and software engineering expertise.
Experience designing scalable, reliable, secure, and maintainable AI-powered systems.
Ability to independently investigate technical problems, navigate ambiguity, and deliver solutions in rapidly changing environments.
Strong communication and collaboration skills, particularly when working with both technical and non-technical stakeholders.
Experience with LangChain or comparable LLM orchestration frameworks is advantageous.
Hands-on experience building Agentic AI systems, autonomous workflows, or AI agents is a plus.
Experience implementing Retrieval-Augmented Generation (RAG), vector databases, embeddings, semantic search, and document-processing pipelines is desirable.
Familiarity with AI evaluation frameworks, observability, guardrails, and LLM application monitoring is beneficial.
Experience with cloud platforms and modern deployment practices such as Docker, Kubernetes, and CI/CD is preferred.
Exposure to multi-agent architectures, tool calling, function calling, and workflow orchestration is an advantage.