Design, build, and deploy production-grade Generative AI solutions for enterprise use cases. Responsibilities include developing RAG pipelines, prompt strategies, agentic workflows, LLM integrations, model fine-tuning, evaluation, monitoring, governance, and LLMOps/MLOps practices. The role collaborates with product, engineering, data, and business teams to deliver scalable, secure, and high-performing AI applications.
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
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