AI Engineer

We are seeking an experienced Senior AI Engineer to join our Engineering Team. The ideal candidate will have deep expertise in Large Language Models (LLMs), OCR, Retrieval Augmented Generation (RAG), and Agentic AI systems, coupled with strong experience in cloud-native, enterprise-scale environments. This role is pivotal in driving our AI innovation strategy, leading end-to-end solution development, and delivering production-grade AI systems.
Responsibilities:
Lead the full lifecycle of Generative AI solutions from design and development to deployment, encompassing LLM-powered workflows, RAG pipelines, OCR, and Agentic AI systems.
Architect and implement secure, scalable AI infrastructures using GCP, AWS, or Azure.
Apply LLMOps best practices, including fine-tuning, advanced prompt engineering, and model optimization, to maximize performance and contextual accuracy.
Design and maintain APIs and microservices (FastAPI, REST, Spring Boot) to integrate AI capabilities into enterprise systems.
Build and optimize data pipelines and manage vector databases and Elasticsearch for efficient knowledge retrieval and decision-making.
Implement MLOps, CI/CD, and DevOps pipelines using Kubernetes, Docker, Jenkins, and Ansible for automated deployment and monitoring.
Ensure system reliability and observability through logging, monitoring, and infrastructure-as-code practices (e. g., ELK stack).
Collaborate cross-functionally with product, engineering, and business teams to align AI solutions with organizational goals.
Mentor and guide junior engineers, setting best practices for scalable, maintainable AI development.
Requirements:
4+ years in software engineering, with 2+ years focused on AI/ML in production.
Proven experience shipping LLM-powered or ML-powered features at scale.
Degree in Computer Science, Computer Engineering, or a related field.
Background in fintech or document-heavy workflows is a strong differentiator.
Deep expertise in LLMs and the full AI stack: prompt engineering, fine-tuning, RAG pipelines, agentic frameworks (LangChain, LlamaIndex, CrewAI, etc. ).
Proficiency in Python; strong engineering fundamentals (system design, APIs, observability).
ML fundamentals: classification, regression, anomaly detection, NLP.
Experience with vector databases (Pinecone, Weaviate, and pgvector).
Cloud-native (AWS/GCP/Azure) deploying and monitoring models in production.
MLOps: experiment tracking (MLflow, W& B), CI/CD for models, drift monitoring.
Preferred / General Requirements:
Experience with enterprise AI security, governance, and compliance frameworks.
Familiarity with frontend integration frameworks (e. g., ReactJS) for AI-enabled applications.
A passion for innovation and staying current with emerging trends in Generative AI and Agentic systems.

Experience
3-7 yrs