AI/ML Engineer

ABOUT NSTARX

NStarX is an AI-first, Cloud-first engineering services company built and led by practitioners. With over a decade of industry experience, we help organizations accelerate business value through AI, ML, Cloud, Automation, and Software Engineering.

Our teams build production-grade solutions that transform complex data into actionable intelligence using modern AI/ML, cloud, and software engineering practices.

ABOUT THE ROLE

We are looking for an AI/ML Engineer who is passionate about building practical AI solutions for real-world production problems.

This is a builder-focused engineering role rather than a research role. You will use Python to integrate AI models into production workflows involving document intelligence, email processing, entity resolution, data enrichment, classification, matching, scoring, and prediction.

You will work with both commercial and open-source foundation models, build reliable AI services around them, and contribute to traditional machine learning initiatives under the guidance of senior engineers.

The ideal candidate is a strong Python engineer who understands modern AI/ML patterns and can turn models into reliable, testable, production-ready services.

AI & GENERATIVE AI ENGINEERING

Build production-quality Python applications that integrate foundation models into automated pipelines and services. Work with commercial and open-source models such as OpenAI, Anthropic Claude, Llama, Mistral, and Qwen. Design effective prompts and implement structured outputs, tool calling, error handling, retries, and guardrails. Build retrieval-based and multi-step AI workflows using frameworks such as LangChain, LangGraph, or equivalent technologies. Integrate AI models into production applications through APIs and backend services.

MACHINE LEARNING

Build and maintain ML models for classification, entity matching, ranking, propensity, likelihood scoring, and prediction. Assist with model training, evaluation, deployment, and performance monitoring under the guidance of senior engineers. Use established ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch. Analyze real-world datasets and identify opportunities where ML or simpler rule-based approaches may be more appropriate.

DATA & DOCUMENT INTELLIGENCE

Develop AI/ML workflows for processing documents, emails, and other unstructured data. Contribute to entity resolution, record linkage, fuzzy matching, information extraction, and data enrichment workflows. Work with retrieval and RAG (Retrieval-Augmented Generation) patterns using vector stores. Support synthetic data generation and validation where real data is limited, sensitive, or contractually restricted.

MLOPS & PRODUCTIONIZATION

Support ML training and inference pipelines in cloud environments. Assist with model versioning, monitoring, drift detection, and performance tracking. Build automated evaluation processes using test datasets, before/after comparisons, and failure analysis. Optimize solutions for reasonable inference latency and token/model costs. Follow software engineering best practices including version control, testing, code reviews, and documentation.

RESPONSIBLE AI & DATA SECURITY

Follow responsible AI and secure data-handling practices. Protect sensitive information including PHI and follow applicable access-control requirements. Maintain documentation around model usage, training data, evaluation, and limitations. Work within established governance and compliance requirements.

REQUIRED SKILLS & EXPERIENCE

2–4 years of experience in Machine Learning, Data Science, Data Engineering, Software Engineering, or a related field. Strong hands-on Python programming skills. Working knowledge of SQL. Experience working with AI/ML solutions in production or near-production environments. Hands-on experience integrating foundation models through APIs. Experience with prompt engineering and structured model outputs. Basic understanding of transformers and modern foundation models.

EXPERIENCE WITH ONE OR MORE ML FRAMEWORKS/LIBRARIES

• scikit-learn
• PYTORCH

Exposure to LangChain, LangGraph, or comparable AI orchestration frameworks. Understanding of RAG and vector database/vector store concepts. Basic exposure to synthetic data generation. Familiarity with at least one major cloud platform; Azure preferred. Strong understanding of software engineering practices including Git, testing, code review, and documentation.

GOOD TO HAVE

Experience with entity resolution, record linkage, or fuzzy matching. Experience with document intelligence, OCR, or information extraction. Experience working with healthcare, insurance, claims, or other regulated datasets. Experience with Azure AI/ML services. Experience building AI agents or multi-step LLM workflows. Experience with vector databases such as Azure AI Search, Pinecone, Weaviate, Milvus, or equivalent. Contributions to open-source AI/ML projects. Experience working with US-based teams in offshore/distributed environments.

REQUIRED COMPETENCIES

Strong Python engineering and problem-solving skills. Analytical thinking and ability to work with ambiguous, messy real-world data. Ability to balance traditional ML, Generative AI, and simpler engineering approaches based on the problem. Strong attention to testing, reliability, and production quality. Good written and verbal communication skills. Ability to work independently in a distributed/remote environment. Willingness to learn new AI/ML technologies and frameworks. Ability to collaborate effectively with senior engineers and cross-functional teams.

EDUCATION

Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related technical field, or equivalent practical experience.

WHAT WE’RE LOOKING FOR

We are looking for an engineer who can move beyond notebooks and prototypes and help turn AI/ML models into reliable production capabilities.