Python Gen AI Developer- Hybrid/Client Site

NTT DATA Services•Pine Grove Township, PA

5d•$75,168 - $113,600•Hybrid

About The Position

We are seeking an experienced Python GenAI Developer to design, develop, and implement generative AI and machine learning solutions that support business and technology objectives. The ideal candidate will have strong Python development skills, hands-on experience with Generative AI/LLMs, and experience building AI-powered applications using modern frameworks, APIs, databases, and cloud technologies. The successful candidate will work closely with engineering, product, and leadership teams throughout the solution lifecycle—from functional and technical design through prototyping, development, testing, deployment, and ongoing optimization.

Requirements

5+ years of overall software development or technology experience.
3+ years of hands-on Python development experience, including libraries such as NumPy and Pandas.
2+ years of hands-on experience with Generative AI and/or LLM technologies, including a solid understanding of underlying concepts and architectures.
Strong experience developing AI/ML applications and solutions using Python.
Hands-on experience with LangChain, embeddings, vector databases, RAG, or related GenAI frameworks and technologies.
Experience with FastAPI, Flask, Django, or another Python-based web development framework.
Experience with machine learning frameworks such as scikit-learn, TensorFlow, PyTorch, or JAX.
Strong experience with MongoDB and Neo4j.
Experience integrating AI/ML capabilities into applications using REST APIs, microservices, and cloud services.
Working knowledge of Google Cloud Platform (GCP) and cloud-based AI/ML services.
Experience working in an Agile/Scrum development environment.
Strong analytical, problem-solving, and troubleshooting skills.
Excellent communication and collaboration skills, with the ability to work effectively with both technical and business stakeholders.

Nice To Haves

Experience developing conversational AI, chatbots, virtual assistants, or IVR solutions.
Experience working within the banking or financial services industry.
Experience with enterprise-scale Generative AI implementations.
Knowledge of knowledge graphs and graph-based AI applications using Neo4j.
Experience with cloud-based MLOps, model deployment, monitoring, and lifecycle management.
Familiarity with emerging LLM platforms, AI agents, prompt engineering, and RAG architectures.

Responsibilities

Design, develop, test, and optimize Generative AI and machine learning solutions using Python and modern AI/ML frameworks.
Develop and integrate LLM-based applications, including conversational AI, retrieval-augmented generation (RAG), embeddings, and vector-based search solutions.
Work with frameworks and technologies such as LangChain, vector databases, embeddings, and related GenAI platforms.
Build production-ready AI applications and services using Python web frameworks such as FastAPI, Flask, or Django.
Integrate AI/ML solutions into enterprise applications through APIs, microservices, and cloud-based platforms.
Apply machine learning techniques using frameworks such as scikit-learn, TensorFlow, PyTorch, or JAX.
Develop and optimize machine learning models, including understanding of LLMs, GANs, VAEs, and other generative AI architectures.
Select and prepare appropriate datasets and determine effective data representation and preprocessing approaches.
Perform model testing, statistical analysis, evaluation, tuning, and retraining to improve performance and reliability.
Extend or integrate existing machine learning libraries, frameworks, and AI services to meet project requirements.
Work with Neo4j and MongoDB to support AI/ML applications, knowledge graphs, and data-driven solutions.
Deploy and integrate AI solutions within cloud environments, with a focus on Google Cloud Platform (GCP).
Collaborate with engineering and leadership teams on functional design, technical design, prototyping, development, testing, deployment, and user training.
Participate in Agile/Scrum ceremonies and contribute to estimation, sprint planning, backlog refinement, demonstrations, and retrospectives.
Stay current with emerging Generative AI, machine learning, LLM, and AI application development technologies and recommend opportunities to incorporate them into the organization's AI strategy.