Senior ML & AI Engineer
Mumbai
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingDesign and build LLM and multi-agent AI systems for production
Build embedding, retrieval, and vector similarity search layers
Lead agentic AI solutions covering orchestration, memory, and tool use
Design confidence-gating and human-in-the-loop escalation logic
Mentor engineers and data scientists on prompts, agents, and evaluation
From the employer’s posting
Architect and build AI-first systems using Large Language Models (LLMs), autonomous multi-agent architectures
Build the representation and retrieval layers that power intelligent systems
Lead the development of Agentic AI solutions — task orchestration, memory and context management, tool invocation
Design and calibrate confidence-gating and escalation logic for autonomous systems
Support and mentor engineers and data scientists in best practices for prompt design, agent workflows, evaluation frameworks
What you’ll bring
All qualificationsCore experience
- 6+ years in ML/AI engineering, with a track record of shipping production systems (not solely research prototypes).
- Experience with agentic systems and multi-agent orchestration (LangGraph, CrewAI, AutoGen) and LLM-based pipelines.
- Strong understanding of ML fundamentals, including regression, classification, forecasting, and model interpretability.
- Proven experience building and deploying deep learning models at scale, including data pipelines, evaluation, and MLOps practices.
- Experience designing human-in-the-loop or active-learning systems, and confidence calibration for autonomous decisioning.
- Hands-on experience with deep learning architectures such as CNN, LSTM, GRU, GANs, BERT, Diffusion Models, and Transformers.
Preferred experience
- Proficiency in Python; familiarity with other programming languages like C++ or Java is a plus.
Qualification wording
6+ years in ML/AI engineering, with a track record of shipping production systems (not solely research prototypes).
Experience with agentic systems and multi-agent orchestration (LangGraph, CrewAI, AutoGen) and LLM-based pipelines.
Strong understanding of ML fundamentals, including regression, classification, forecasting, and model interpretability.
Proven experience building and deploying deep learning models at scale, including data pipelines, evaluation, and MLOps practices.
Experience designing human-in-the-loop or active-learning systems, and confidence calibration for autonomous decisioning.
Hands-on experience with deep learning architectures such as CNN, LSTM, GRU, GANs, BERT, Diffusion Models, and Transformers.
Proficiency in Python; familiarity with other programming languages like C++ or Java is a plus.
Education & alternatives
Who are you? - Candidates with a Master's should have 2–7 years of experience. - Ph.D. holders may have 2–5 years of relevant experience.
- Candidates with a Master's should have 2–7 years of experience. - Ph.D. holders may have 2–5 years of relevant experience. - Strong understanding of ML fundamentals, including regression, classification, forecasting, and model interpretability.
Tools in this posting
- C++
- Java
- Python
- Spark
- PyTorch
- scikit-learn
- TensorFlow
- XGBoost
Source — Tool mentions in context
- Design and lead experiments across advanced AI/ML areas including computer vision, NLP, and LLMs, and emerging domains such as distributed computing, 3D modeling, AR/VR, LiDAR/drone data processing, and quantum computing (good to have). - Write efficient, production-quality code in Python, with working knowledge of C++ or Java where needed; leverage frameworks such as PyTorch, TensorFlow, and Spark for large-scale ML training and inference. - Define and track KPIs at both individual and business-unit levels in partnership with the manager, ensuring impact-driven innovation.
Nice to have - Working knowledge of C++ or Java. - Exposure to emerging domains: quantum computing, AR/VR, 3D modeling, LiDAR / drone data processing.
- Exposure to multimodal / vision-language models — jointly encoding imagery with text or structured data is a strong plus. - Proficiency in Python; familiarity with other programming languages like C++ or Java is a plus. - Familiarity with ML frameworks such as PyTorch, TensorFlow, scikit-learn, and experience working with large datasets and distributed systems.
- Hands-on experience with LLMs and modern transformer architectures. - Proficiency in Python and modern ML frameworks (PyTorch and/or TensorFlow). - Ability to own ambiguous, end-to-end problems and make independent architectural decisions.
- Experience designing human-in-the-loop or active-learning systems, and confidence calibration for autonomous decisioning. - Large-scale data processing (e.g. Spark) and vector databases. Nice to have
- Proficiency in Python; familiarity with other programming languages like C++ or Java is a plus. - Familiarity with ML frameworks such as PyTorch, TensorFlow, scikit-learn, and experience working with large datasets and distributed systems. - Experience with geospatial or spatial data spatial indexing, geospatial data structures, or entity resolution / conflation is a strong advantage, as the work centers on spatial reasoning at scale.
As a Senior ML & AI Engineer, you will help shape the next generation of AI-powered products and platforms at HERE. You will design, develop, and deploy intelligent systems that combine Generative AI, Agentic AI, machine learning, and advanced retrieval techniques to solve complex business and spatial challenges. You will have the opportunity to: - Architect and build AI-first systems using Large Language Models (LLMs), autonomous multi-agent architectures, and classical ML models such as regression, clustering, XGBoost, SVM, and tree-based learners. - Build the representation and retrieval layers that power intelligent systems — designing embedding models, unified latent-space representations across heterogeneous signals, vector similarity search, and retrieval-augmented approaches at scale.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 12, 2026 · 17 days ago
- Recorded sightings
- 20
- Last seen by us
- Oct 29, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
Complete your application on careers-here.icims.com. The employer’s form will show what is required.
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Job description
What's the role?
As a Senior ML & AI Engineer, you will help shape the next generation of AI-powered products and platforms at HERE. You will design, develop, and deploy intelligent systems that combine Generative AI, Agentic AI, machine learning, and advanced retrieval techniques to solve complex business and spatial challenges. You will have the opportunity to:
- Architect and build AI-first systems using Large Language Models (LLMs), autonomous multi-agent architectures, and classical ML models such as regression, clustering, XGBoost, SVM, and tree-based learners.
- Build the representation and retrieval layers that power intelligent systems — designing embedding models, unified latent-space representations across heterogeneous signals, vector similarity search, and retrieval-augmented approaches at scale.
- Lead the development of Agentic AI solutions — task orchestration, memory and context management, tool invocation, and human-agent collaboration — using frameworks such as LangGraph, CrewAI, and AutoGen.
- Design and deliver end-to-end Generative AI applications for text generation, summarization, code synthesis, image generation, and multi-modal interfaces, optimizing prompt engineering and model fine-tuning.
- Develop multimodal and vision-language systems that jointly encode imagery with text and structured attributes for detection, classification, and semantic reasoning.
- Apply deep learning techniques including CNNs, RNNs (LSTM, GRU), GANs, diffusion models, transformers, and graph neural networks to real-world tasks in vision, language, and planning.
- Design and calibrate confidence-gating and escalation logic for autonomous systems, including human-in-the-loop and active-learning workflows.
- Collaborate across teams to translate domain problems into scalable, explainable, and production-ready AI solutions, incorporating responsible AI principles.
- Stay at the forefront of AI research by evaluating and implementing emerging models, agentic frameworks, and open-source innovations, ensuring technical currency in a fast-evolving space.
- Design and lead experiments across advanced AI/ML areas including computer vision, NLP, and LLMs, and emerging domains such as distributed computing, 3D modeling, AR/VR, LiDAR/drone data processing, and quantum computing (good to have).
- Write efficient, production-quality code in Python, with working knowledge of C++ or Java where needed; leverage frameworks such as PyTorch, TensorFlow, and Spark for large-scale ML training and inference.
- Define and track KPIs at both individual and business-unit levels in partnership with the manager, ensuring impact-driven innovation.
- Support and mentor engineers and data scientists in best practices for prompt design, agent workflows, evaluation frameworks, and research translation.
- Contribute to HERE's innovation footprint through patents, internal IP development, and peer-reviewed publications.
Qualifications
- 6+ years in ML/AI engineering, with a track record of shipping production systems (not solely research prototypes).
- Strong foundation in representation learning and embeddings, vector similarity search, and retrieval-augmented approaches.
- Proven experience building and deploying deep learning models at scale, including data pipelines, evaluation, and MLOps practices.
- Hands-on experience with LLMs and modern transformer architectures.
- Proficiency in Python and modern ML frameworks (PyTorch and/or TensorFlow).
- Ability to own ambiguous, end-to-end problems and make independent architectural decisions.
Preferred
- Experience with agentic systems and multi-agent orchestration (LangGraph, CrewAI, AutoGen) and LLM-based pipelines.
- Multimodal / vision-language model experience — jointly encoding imagery with text or structured data.
- Geospatial experience — working with spatial data, spatial indexing, geospatial data structures, or entity resolution / conflation over spatial records. (Strongly valued: the work centers on spatial reasoning at scale.)
- Experience designing human-in-the-loop or active-learning systems, and confidence calibration for autonomous decisioning.
- Large-scale data processing (e.g. Spark) and vector databases.
Nice to have
- Working knowledge of C++ or Java.
- Exposure to emerging domains: quantum computing, AR/VR, 3D modeling, LiDAR / drone data processing.
- Patents, internal IP, or peer-reviewed publications.
Who are you?
- Candidates with a Master's should have 2–7 years of experience.
- Ph.D. holders may have 2–5 years of relevant experience.
- Strong understanding of ML fundamentals, including regression, classification, forecasting, and model interpretability.
- Solid grounding in representation learning and embeddings, vector similarity search, and retrieval-augmented approaches the core of large-scale foundation-model work.
- Hands-on experience with deep learning architectures such as CNN, LSTM, GRU, GANs, BERT, Diffusion Models, and Transformers.
- Experience working with LLMs (e.g., GPT-3/4, Claude, Mistral) and building prompt-based or agent-based solutions using frameworks like LangGraph, CrewAI, or AutoGen.
- Exposure to multimodal / vision-language models — jointly encoding imagery with text or structured data is a strong plus.
- Proficiency in Python; familiarity with other programming languages like C++ or Java is a plus.
- Familiarity with ML frameworks such as PyTorch, TensorFlow, scikit-learn, and experience working with large datasets and distributed systems.
- Experience with geospatial or spatial data spatial indexing, geospatial data structures, or entity resolution / conflation is a strong advantage, as the work centers on spatial reasoning at scale.
- Curiosity and exposure to adjacent technologies like TinyML, Quantum Computing, or 3D modeling/AR is an advantage.
- Excellent problem-solving skills and the ability to work in cross-functional, agile teams.
HERE is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, age, gender identity, sexual orientation, marital status, parental status, religion, sex, national origin, disability, veteran status, and other legally protected characteristics.
Who are we?
HERE Technologies is a location data and technology platform company. We empower our customers to achieve better outcomes – from helping a city manage its infrastructure or a business optimize its assets to guiding drivers to their destination safely.
At HERE we take it upon ourselves to be the change we wish to see. We create solutions that fuel innovation, provide opportunity and foster inclusion to improve people’s lives. If you are inspired by an open world and driven to create positive change, join us. Learn more about us on our YouTube Channel.
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Mumbai
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