Developer L4

Wipro•Minneapolis, MN

6h•Remote

About The Position

We are seeking an AI/ML Engineer with a strong blend of Full-Stack Software Engineering and Applied AI expertise to design, build, and optimize Generative AI-powered applications. The role involves developing scalable AI solutions, integrating LLMs into enterprise applications, implementing agentic workflows, and delivering secure, production-grade solutions that drive business value.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, AI, or a related field.
4–10 years of experience in Applied AI, Machine Learning, Software Engineering, and Digital Transformation.
Strong experience designing and deploying enterprise-scale AI solutions using Generative AI, LLMs, AI Agents, and Intelligent Automation.
Proficiency in Python (mandatory) and experience with Java and React JS preferred.
Hands-on expertise in PyTorch, SQL, OpenAI APIs, FastAPI, Flask, Streamlit, REST APIs, Docker, and modern application architectures.
Strong understanding of machine learning fundamentals, deep learning, statistics, optimization, feature engineering, and model development.
Experience with NLP/NLU, semantic search, transformer architectures, embeddings, and model interpretability.
Knowledge of Azure, AWS, and GCP cloud AI services.
Ability to communicate technical AI concepts effectively to business and leadership stakeholders.
GenAI Consulting for Apps & Infra
Experience: 5-8 Years

Nice To Haves

MLOps, React JS , Java Backend , CI/CD for ML, model monitoring, lifecycle management, Kubernetes, Docker, and JFrog.
Experience deploying ML models to production environments.
Distributed ML and scalable data platforms (e.g., Spark).
Multimodal AI, including Computer Vision and Speech Recognition (ASR).
Knowledge of AI governance, Responsible AI, security, compliance, and model lifecycle management.
Experience in healthcare, life sciences, payer/provider, or other regulated industries.
Experience leading Agile teams and collaborating across cross-functional stakeholders.

Responsibilities

Develop and enhance AI-powered features such as chatbots, summarization, knowledge search, and data extraction using LLMs.
Design and implement RAG (Retrieval-Augmented Generation) solutions, including data preprocessing, embeddings, vector search, and retrieval optimization.
Build and integrate AI services using OpenAI, Azure AI, and open-source LLMs.
Develop agentic AI workflows, multi-agent frameworks, and intelligent automation solutions.
Improve model performance, reliability, and accuracy through prompt engineering, evaluation, guardrails, and fine-tuning techniques.
Work with vector databases such as FAISS, Pinecone, and pgvector.
Optimize AI applications for latency, throughput, scalability, and cost efficiency using caching, batching, quantization, and model optimization techniques.
Collaborate with product, data, and engineering teams to deliver high-quality AI solutions.
Participate in architecture discussions, code reviews, and knowledge-sharing initiatives.