Honeywell

Diversified industrial conglomerate: aerospace, automation, safety

Advanced Software Engineer

Applied Machine Learning +1Confirmed live in the last 24 hours

Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

Strong understanding of supervised and unsupervised learning, regression and classification, clustering, recommendation systems, time-series forecasting, reinforcement learning, and deep learning.
Experience with large language models, retrieval-augmented generation architectures, prompt engineering, fine-tuning and parameter-efficient fine-tuning techniques, LangChain or LangGraph, Semantic Kernel, AI agents, and multi-agent systems.
Expert-level Python development.
Strong knowledge of NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch.
Experience with Docker, Kubernetes, MLflow, continuous integration and continuous delivery pipelines, model monitoring, and Azure ML, AWS SageMaker, or Vertex AI.
Knowledge of SQL, Spark or PySpark, data engineering concepts, ETL pipelines, and feature stores.
Experience with vector databases such as Pinecone, ChromaDB, Weaviate, Azure AI Search, or FAISS.
Experience with at least one of Microsoft Azure AI Services, Azure OpenAI, AWS AI/ML Services, or Google Vertex AI.

Responsibilities

Design, develop, train, evaluate, and deploy machine-learning and deep-learning models.
Develop and optimize generative AI applications using large language models such as GPT, Llama, Claude, or Mistral.
Build retrieval-augmented generation solutions using vector databases.
Fine-tune foundation models and implement prompt-engineering strategies.
Create scalable AI pipelines for data ingestion, feature engineering, model training, and inference.
Develop AI-powered applications, chatbots, recommendation engines, and predictive analytics solutions.
Implement MLOps practices including automated training, deployment, monitoring, and retraining.
Work with cross-functional teams to translate business requirements into AI solutions.
Optimize model performance, latency, accuracy, and cost efficiency.
Ensure AI governance, model explainability, privacy, and responsible AI practices.
Stay current with emerging AI technologies and recommend innovations.

Required Skills & Qualifications

Technical Skills

Machine Learning & AI
Generative AI
Strong understanding of: Supervised and Unsupervised Learning, Regression and Classification, Clustering, Recommendation Systems, Time-Series Forecasting, Reinforcement Learning, Deep Learning
Experience with: LLMs (GPT, Llama, Gemini, Claude, Mistral), RAG architectures, Prompt Engineering, Fine-tuning and PEFT techniques, LangChain / LangGraph, Semantic Kernel, AI Agents and Multi-Agent Systems
Programming
Expert-level Python development
Strong knowledge of: NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch
MLOps & Deployment: Docker, Kubernetes, MLflow, CI/CD pipelines, Model Monitoring, Azure ML / AWS SageMaker / Vertex AI
Data Technologies: SQL, Spark/PySpark, Data Engineering concepts, ETL pipelines, Feature Stores
Vector Databases: Pinecone, ChromaDB, Weaviate, Azure AI Search, FAISS
Cloud Platforms
Experience in at least one:

Microsoft Azure AI Services

Azure OpenAI

AWS AI/ML Services

Google Vertex AI

Key Responsibilities

Design, develop, train, evaluate, and deploy machine learning and deep learning models.
Develop and optimize Generative AI applications using Large Language Models (GPT, Llama, Claude, Mistral, etc.).
Build Retrieval-Augmented Generation (RAG) solutions using vector databases.
Fine-tune foundation models and implement prompt engineering strategies.
Create scalable AI pipelines for data ingestion, feature engineering, model training, and inference.
Develop AI-powered applications, chatbots, recommendation engines, and predictive analytics solutions.
Implement MLOps practices including automated training, deployment, monitoring, and retraining.
Work with cross-functional teams to translate business requirements into AI solutions.
Optimize model performance, latency, accuracy, and cost efficiency.
Ensure AI governance, model explainability, privacy, and responsible AI practices.
Stay current with emerging AI technologies and recommend innovations.

About the Company

Honeywell

Diversified industrial conglomerate: aerospace, automation, safety

Honeywell operates in four main areas—aerospace, building automation, performance materials and technologies (PMT), and safety and productivity solutions (SPS). Its products couple hardware, software, and services, delivering integrated solutions for customers in design, manufacturing, installation, maintenance, and analytics. It stands out through a broad, diversified portfolio, a global presence, and the ability to connect multiple stages of the product lifecycle into one ecosystem. The company aims to help customers operate more reliably and efficiently, reduce costs and environmental impact, and grow recurring revenue by offering solutions, services, and digital platforms that link its hardware and software offerings.

Company size: 10,001+
Company stage: IPO
Headquarters: Charlotte, NC
Founded: 1906

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