This role is for one of Weekday’s clients
Salary range: Rs 3500000 - Rs 5500000 (ie INR 35 - 55 LPA)

Min Experience: 7+ years
Location: Mumbai, Maharashtra, India
JobType: full-time

We are seeking a highly skilled and hands-on AI Engineer to design, build, and deploy production-grade Generative AI solutions for complex enterprise use cases. This role requires strong expertise in Artificial Intelligence, Machine Learning, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and modern AI application development.

The ideal candidate will have a strong engineering mindset and a proven ability to take AI solutions from concept to production. You will work closely with cross-functional teams, including product, engineering, data, and business stakeholders, to develop scalable, reliable, and high-performing AI applications that deliver measurable business outcomes.

Requirements

Key Responsibilities

• Design, develop, and lead end-to-end implementation of enterprise-grade Generative AI solutions.
• Build scalable and production-ready AI applications for real-world business use cases.
• Design and optimize RAG pipelines, including data ingestion, chunking, embeddings, retrieval, reranking, and grounded response generation.
• Develop effective prompt engineering strategies to improve response quality, reliability, consistency, and task performance.
• Work on model fine-tuning and adaptation techniques to improve model performance for domain-specific use cases.
• Build and implement Agentic AI workflows involving tools, APIs, memory, reasoning, and multi-step execution.
• Develop AI solutions using LLM orchestration frameworks for workflow management, tool calling, and multi-agent coordination.
• Integrate Generative AI applications with enterprise platforms, internal applications, APIs, databases, and cloud services.
• Evaluate and optimize AI models based on quality, accuracy, latency, hallucination risks, safety, scalability, and cost-performance trade-offs.
• Develop reusable frameworks, standards, and best practices for designing, building, and deploying AI solutions at scale.
• Collaborate closely with data, engineering, product, and business teams to identify use cases and rapidly move solutions from concept to deployment.
• Implement AI application evaluation, monitoring, observability, and governance practices.
• Ensure AI solutions follow enterprise security, responsible AI, and compliance requirements.
• Contribute to LLMOps and MLOps practices, including model monitoring, deployment, versioning, and lifecycle management.

Required Skills and Qualifications

• 7–10 years of experience in AI/ML engineering, applied AI, intelligent application development, or related fields.
• At least 3 years of hands-on experience in Generative AI.
• Proven experience designing and deploying at least one Generative AI solution into a production environment.
• Strong understanding of AI and Machine Learning fundamentals.
• Hands-on expertise with Large Language Models (LLMs) and modern AI application architectures.
• Strong experience with RAG, Prompt Engineering, embeddings, semantic search, and vector databases.
• Experience building Agentic AI applications and multi-step AI workflows.
• Knowledge of model fine-tuning and adaptation techniques.
• Strong programming skills in Python.
• Experience with modern AI and LLM application frameworks and orchestration tools.
• Experience with cloud-based AI services, preferably AWS Generative AI services.
• Good understanding of LLM application design, evaluation, observability, deployment, and performance optimization.
• Familiarity with APIs, enterprise system integrations, and cloud-based architectures.
• Understanding of AI security, responsible AI practices, governance, and risk management.
• Experience with LLMOps/MLOps, monitoring, and AI lifecycle management is preferred.

Must-Have Skills

• Large Language Models (LLMs)
• Artificial Intelligence (AI)
• Retrieval-Augmented Generation (RAG)

Preferred Skills

• Python
• Prompt Engineering
• Agentic AI
• Vector Databases
• Embeddings and Semantic Search
• AWS Generative AI Services
• LLMOps / MLOps
• AI Orchestration Frameworks
• Model Fine-Tuning