867 Machine Learning Engineer jobs in Delhi

Job Title: H&E Image Analysis Scientist / Machine Learning Engineer- Spatial Omics (PhD)

Experience: Freshers

Location: Delhi

Job Description:

We are seeking a motivated PhD candidate interested in machine learning for histopathology

image analysis. The candidate will contribute to developing and optimizing deep learning

models to analyze digitized H&E slides for cancer classification and spatial mapping. This

role is well-suited for researchers aiming to apply advanced computational methods to

biomedical challenges.

Responsibilities:

● Design, develop, and train convolutional neural networks (CNNs) and related ML

models on H&E-stained histology images.

● Use and extend tools such as QuPath for cell annotations, segmentation models, and

dataset curation.

● Preprocess, annotate, and manage large image datasets to support model training

and validation.

● Collaborate with cross-disciplinary teams to integrate image-based predictions with

molecular and clinical data.

● Analyze model performance and contribute to improving accuracy, efficiency, and

robustness.

● Document research findings and contribute to publications in peer-reviewed journals.

Qualifications:

● PhD in Computer Science, Biomedical Engineering, Data Science, Computational

Biology, or a related discipline.

● Demonstrated research experience in machine learning, deep learning, or biomedical

image analysis (e.g., publications, thesis projects, or conference presentations).

● Strong programming skills in Python and experience with ML frameworks such as

TensorFlow or PyTorch.

● Familiarity with digital pathology workflows, image preprocessing/augmentation, and

annotation tools.

● Ability to work collaboratively in a multidisciplinary research environment.

Preferred:

● Background in cancer histopathology or biomedical image analysis.

● Knowledge of multimodal data integration, including spatial transcriptomics.

Hiring: ML & Data Engineers (AI-Native Fashion) @Dripup

Location:

New Delhi

About Dripup:

Dripup is an AI-native fashion discovery platform that's solving the core problems leading to shopping and selling experience gaps in the online ecosystem.

Our mission is to transform how users discover and interact with fashion, making it more intuitive, personalized, and efficient.

Basically we are making shopping fashion online feel like "wow, that's exactly what I've been looking for" instead of feeling like finding a needle in a haystack./

Your Opportunity:

We are looking for talented and passionate
ML & Data Engineers

to join our team and play a pivotal role in building the technical foundation of Dripup. You'll work on critical data infrastructure and core AI components, directly impacting our user experience and platform performance.

What You'll Be Doing:

• Design & Build Data Infrastructure:

Architect and implement the most optimal way to store and manage user data, ensuring high performance and scalability as our platform grows.
- Create Fashion Data Pipelines:

Develop robust data storage solutions for cataloging and managing fashion brands' clothing data.
- Build an AI Fashion Stylist:

Take the lead in fine-tuning existing open-source Large Language Models (LLMs) to create a next-generation AI-powered fashion stylist.
- Tackling Challenges:

We're a startup, which means you'll be solving complex, real-world problems every day, with the freedom to find the most elegant and efficient solutions.

Who You Are:

• You have a strong background in Machine Learning or Data Engineering, with a focus on building production-level systems.
• You're experienced in designing and implementing scalable data storage solutions (e.g., SQL, NoSQL, data lakes).
• You have hands-on experience with LLMs and are comfortable with fine-tuning and deploying them for specific applications.
• You are a problem-solver who enjoys tackling complex challenges and finding elegant, efficient solutions.
• You are passionate about the intersection of AI and fashion, and excited to build the future of online shopping.

How to Apply:

Simply j
ust DM me your resume and a short message

about why you're a great fit for Dripup.

About the Role:

We are looking for an experienced Machine Learning Engineer to design, build, and deploy ML models that power business-critical solutions. You will collaborate with cross-functional teams to deliver scalable ML systems, driving innovation and measurable business outcomes.

Key Responsibilities:

• Design, develop, and deploy ML models (regression, classification, clustering, deep learning).
• Build and optimize data pipelines, feature engineering, and training workflows.
• Deploy models to production using Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).
• Monitor model performance, retrain, and maintain production-ready systems.
• Develop APIs and integrate ML solutions with enterprise applications.
• Stay updated on ML/AI trends and contribute innovative solutions.
• Collaborate with data scientists, engineers, and product managers to align with business goals.

Qualifications:

• 3+ years of experience in ML engineering or related field.
• Strong proficiency in
  Python
  ; knowledge of Java/Scala/R is a plus.
• Expertise with ML libraries/frameworks:
  scikit-learn, TensorFlow, PyTorch, Keras
  .
• Hands-on with data tools:
  Pandas, NumPy, Spark
  .
• Experience with ML deployment (SageMaker, Azure ML, GCP AI, TensorFlow Serving, TorchServe).
• Familiarity with databases (
  SQL, MongoDB, Cassandra
  ) and version control (
  Git
  ).
• Understanding of DevOps/MLOps practices.
• Excellent problem-solving, communication, and collaboration skills.

InnerGize
is a consumer health and wellness platform that uses wearable patches to help people manage stress, sleep and mental performance. We are building algorithms that make our product more accurate and user-friendly.

We are looking for a
Machine Learning Engineer
with 2–4 years of experience to design and deploy
convolutional neural network (CNN) algorithms
that can detect and validate correct patch placement based on sensor data.

This position focuses on developing and refining CNN-based models from data collection through to deployment.

Job Requirements

:

• Design, train, and optimize CNN architectures for correct wearable patch placement detection using available sensor data.
• Build and maintain data pipelines for collection, labeling, augmentation, and validation of training datasets.
• Evaluate model performance and iterate to improve accuracy and robustness across users.
• Prepare models for deployment on mobile or embedded systems (optimizing for latency and size).
• Document experiments, models, and performance metrics clearly for internal teams.
• Collaborate with data, product and firmware teams to integrate model outputs into the InnerGize ecosystem.

Education Requirements:

• B.Tech / B.E. in Computer Science or a related field with specialization in Machine Learning, OR
• Master's / Ph.D. in Machine Learning, Data Science, or Applied Mathematics (preferred).
• Experience:
• 2–4 years of professional experience developing machine learning models, with a strong focus on CNN architectures.
• Technical Skills:
• Proficiency in Python and frameworks such as PyTorch or TensorFlow.
• Demonstrated experience designing, training, and deploying CNNs for classification or regression tasks.
• Ability to work with real-world datasets, including preprocessing and augmentation.
• Familiarity with on-device or edge deployment tools such as TensorFlow Lite or ONNX (a plus).
• Soft Skills:
• Strong analytical and problem-solving skills.
• Good communication and documentation abilities.
• Comfortable working cross-functionally with other teams.

Preferable Skills:

• Exposure to time-series or sensor-based data.
• Understanding of mathematical/statistical models for signal behavior.
• Prior experience deploying models on constrained devices.

CTC:
₹12–18 LPA
, commensurate with experience and expertise.

We are building a distributed LLM inference network that combines idle GPU capacity from around the world into a single cohesive plane of compute that can be used for running large-language models like DeepSeek and Llama 4. At any given moment, we have over 5,000 GPUs and hundreds of terabytes of VRAM connected to the network.

We are a small, well-funded team working on difficult, high-impact problems at the intersection of AI and distributed systems. We primarily work in-person from our office in downtown San Francisco.

**Responsibilities

**

• Design and implement optimization techniques to increase model throughput and reduce latency across our suite of models

• Deploy and maintain large language models at scale in production environments

• Deploy new models as they are released by frontier labs

• Implement techniques like quantization, speculative decoding, and KV cache reuse

• Contribute regularly to open source projects such as SGLang and vLLM

• Deep dive into underlying codebases of TensorRT, PyTorch, TensorRT-LLM, vLLM, SGLang, CUDA, and other libraries to debug ML performance issues

• Collaborate with the engineering team to bring new features and capabilities to our inference platform

• Develop robust and scalable infrastructure for AI model serving

• Create and maintain technical documentation for inference systems

**Requirements

**

• 3+ years of experience writing high-performance, production-quality code

• Strong proficiency with Python and deep learning frameworks, particularly PyTorch

• Demonstrated experience with LLM inference optimization techniques

• Hands-on experience with SGLang and vLLM, with contributions to these projects strongly preferred

• Familiarity with Docker and Kubernetes for containerized deployments

• Experience with CUDA programming and GPU optimization

• Strong understanding of distributed systems and scalability challenges

• Proven track record of optimizing AI models for production environments

Nice to Have

• Familiarity with TensorRT and TensorRT-LLM

• Knowledge of vision models and multimodal AI systems

• Experience implementing techniques like quantization and speculative decoding

• Contributions to open source machine learning projects

• Experience with large-scale distributed computing

**Compensation

**We offer competitive compensation, equity in a high-growth startup, and comprehensive benefits. The base salary range for this role is

What does day-to-day look like:

• Develop and maintain DS/ML solutions, including data pipelines, model training, evaluation, and optimization.
• Collaborate with business stakeholders to gather and clarify requirements
• Translate business requirements into DS/ML code that accurately reflects business logic
• Write efficient, maintainable, and well-documented DS/ML solutions
• Participate in code reviews and follow established development standards
• Effectively analyze and select the best algorithms, optimize DS/ML solutions for performance improvement and accuracy
• Create and maintain technical documentation for developed solutions
• Support testing activities and resolve data-related issues

Required Qualifications:

• Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related quantitative field.
• 5+ years of hands-on DS/ML development experience

• Proficiency in at least some of the DS/ML areas and frameworks, including:

• Supervised learning (classification, regression, )

• Unsupervised learning (clustering, anomaly detection, )
• Time-series analysis
• Natural Language Processing (NLP)
• Computer Vision (CV)

• Statistical modeling

• Ability to understand and apply different models to real-world use cases

• Hands-on experience with DS and ML solutions in production environments
• Strong understanding of data cleaning and wrangling, feature engineering, model optimization, and evaluation metrics
• Proficiency in Python and its common data science libraries (e.g., Pandas, NumPy, Scikit-learn)
• Strong knowledge of data analysis pipelines and data visualization techniques
• Experience translating business requirements into DS/ML solutions

Preferred Qualifications:

• Proven expertise in Deep learning (e.g., convolutional neural networks, recurrent neural networks, transformers).
• Experience with cloud data platforms (Databricks, AWS, etc.)
• Knowledge of MLOps principles and tools for model deployment and monitoring
• Hands-on experience with PySpark and Databricks Platform
• Stay up-to-date with the latest advancements in machine learning and artificial intelligence.
• Bonus: Experience and knowledge in Kaggle competitions and Benchmarks, such as MLEBench

Job Description – NLP Expert (Transformers & Custom Architectures)

Position:
NLP Expert

Experience Required:
5
+ Years

Location:
New Delhi (Onsite)

Domain Focus:
Defence Technology & Naval Applications

About Us

At
Crimson Energy Experts Pvt. Ltd.
, we are developing advanced defence-grade software systems to strengthen the future of India's naval and maritime operations. Our projects focus on
Defence Technology, Artificial Intelligence, and Advanced Analytics
, with a mission to empower the
Indian Navy
through next-generation NLP, AI, and data-driven decision-making.

We are looking for an
NLP Expert
with deep expertise in
Hugging Face Transformers
and hands-on experience in
pretraining and developing custom encoding architectures
. This role is critical to building
domain-specific language models
for secure and high-performance defence applications.

Key Responsibilities

• Pretrain and fine-tune
  Hugging Face Transformers
  for
  naval and defence-specific use cases
  .
• Design and implement
  custom encoding architectures
  for unstructured defence data (text, intelligence reports, maritime logs, etc.).
• Develop
  domain-specific embeddings
  and representations for secure and reliable software applications.
• Build
  end-to-end NLP pipelines
  : data collection, preprocessing, model training, evaluation, and deployment.
• Collaborate with engineers and defence domain experts to align NLP solutions with
  operational requirements
  .
• Optimize models for
  performance, scalability, and low-latency inference
  in mission-critical environments.
• Ensure all solutions comply with
  security, confidentiality, and defence-grade standards
  .

Required Skills & Qualifications

• 4+ years
  of experience in
  Natural Language Processing (NLP)
  and
  Deep Learning
  .
• Strong expertise with
  Hugging Face Transformers
  (BERT, GPT, RoBERTa, LLaMA, etc.).
• Experience in
  pretraining LLMs
  and creating
  custom tokenizers/encoding architectures
  .
• Proficiency in
  Python
  , PyTorch, and TensorFlow.
• Solid background in
  text representation learning, embeddings, and sequence modeling
  .
• Experience in
  distributed training
  (multi-GPU/TPU setups).
• Familiarity with
  MLOps practices
  for deploying and monitoring NLP systems.
• Ability to design solutions with
  security-first principles
  for sensitive defence applications.

What We Offer

• An opportunity to
  build indigenous NLP systems
  for the
  Indian Navy
  .
• Work on
  cutting-edge NLP & AI projects
  that directly impact national security.
• Competitive pay with performance-driven growth.
• A chance to be part of a
  mission-driven team
  solving real-world defence challenges.

Job Description – Lead / Senior Machine Learning Engineer

We are looking for an experienced
Lead / Senior Machine Learning Engineer
who can take ownership of designing, developing, and deploying
Computer Vision solutions
in production environments. This is a leadership role where you will work on cutting-edge AI applications while guiding and mentoring a team of ML engineers.

Responsibilities

• Design, develop, and deploy production-grade ML/Computer Vision projects such as
  object detection, defect detection, facial recognition, and human activity analysis
  .
• Build and optimize robust data pipelines for image and video processing across edge and cloud environments.
• Architect scalable real-time AI systems, ensuring high accuracy and low latency.
• Lead and mentor a team of junior ML engineers, driving collaboration and high technical standards.
• Stay updated with the latest research in AI/ML and integrate innovative models into existing workflows.
• Communicate effectively with stakeholders, translating technical concepts into actionable solutions.

Qualifications

• 5+ years of experience in Machine Learning, with proven expertise in
  Computer Vision
  projects deployed in production.
• Strong proficiency in Python, and experience with deep learning frameworks (PyTorch, TensorFlow) and CV libraries (OpenCV).
• Solid knowledge of
  MLOps workflows
  , data pipelines, and scalable deployment strategies.
• Prior experience in leading ML teams and delivering projects end-to-end.
• Excellent communication skills and ability to manage cross-functional collaboration.

Good to Have:

• Experience with vision-based automation in manufacturing.
• Exposure to Generative AI workflows and multi-modal AI pipelines.
• Understanding of AI governance, compliance, and security protocols.

Why Join Us?

• Opportunity to work on challenging AI problems that demand innovation.
• Lead end-to-end AI projects with full ownership and visible impact.
• Be part of a flexible, collaborative, and high-performing team culture with room for growth.