Job Description
As an Applications Dev Analyst here at Honeywell, you will lead application development, collaborate with teams, ensure coding standards, and mentor juniors. Your role will drive innovative software solutions and shape our tech landscape.
Responsibilities
KEY Responsibilities
- Apply strong expertise across Data Science , Machine Learning, and Generative AI to design, develop, and productionize advanced analytical, predictive, and GenAI‑driven solutions, grounded in statistical modeling and data thoughtfulness
- Build end‑to‑end ML and GenAI pipelines on Databricks , and own the MLOps lifecycle using MLflow , ensuring alignment with business objectives and successful delivery of actionable insights
- Design and implement and productionize Agentic AI solutions, including multi‑agent workflows, tool‑calling agents, and autonomous decision pipelines, to address complex business use cases
- Ensure ML and GenAI model performance, uptime, scalability, observability , and monitoring , maintaining high standards of code quality and thoughtful system and model design
- Build and maintain production‑grade CI/CD pipelines for ML and GenAI workloads using GitHub Actions and Databricks Workflows
- Apply strong thoughtfulness and problem‑solving skills to address complex data and AI demands, including LLM fine‑tuning, prompt engineering, RAG optimization, and agent orchestration, to generate valuable business insights
- Collaborate with cross‑functional teams and mentor junior data scientists, clearly communicating Data Science, ML, and GenAI concepts to both technical and non‑technical stakeholders
Qualifications
MUST HAVE
- Bachelor's degree or Advanced degree in Computer Science, Statistics, Mathematics, or related discipline
- 5–8 years of strong experience in Data Science and Machine Learning, including Time‑series forecasting, Regression, Classification, Clustering, Deep Learning, NLP, and Optimization algorithms, using Python in a programming‑intensive role
- 5–8 years of strong experience in Python and PySpark coding, preferably in large‑scale, distributed data environments
- 5–8 years of hands‑on experience with Azure or AWS Databricks, including Spark optimization, Databricks Workflows, and production deployments
- 4+ years of experience in end‑to‑end ML model development and MLOps architecture, including model deployment, monitoring, and lifecycle management
- Strong expertise in MLflow for experiment tracking, model registry, versioning, governance, and production model deployments
- 5-8 years of industry experience with popular ML frameworks such as Keras, TensorFlow, PyTorch, HuggingFace Transformers, and libraries like scikit‑learn
- Strong hands‑on experience across Generative AI and advanced ML domains, including Large Language Models (LLMs), Retrieval‑Augmented Generation (RAG) systems, prompt engineering, embedding strategies, and applied NLP
- Hands‑on experience designing and building Agentic AI solutions using modern agent orchestration and tool‑calling frameworks such as LangChain, LangGraph, CrewAI, or equivalent frameworks
- Strong experience with GitHub Actions, CI/CD pipelines, and MLOps frameworks
- Excellent knowledge and experience or exposure to the Dataiku platform
- Excellent communication skills with a strong people‑oriented, collaborative, and mentoring mindset
