Senior Robotics Engineer – Perception

AuxoAI is hiring a Senior Applied AI Engineer to design and deploy production-grade computer vision systems that operate reliably in real-world environments.

This role focuses on building end-to-end visual intelligence systems, combining deep learning, classical computer vision techniques, and multimodal models. It is not limited to model training and requires strong ownership of system design, deployment, and real-world performance.

You will work on systems that perform perception, understanding, and reasoning over visual data, and integrate these capabilities into larger AI platforms and agent-based workflows.

You will also work on problems where existing approaches may not be sufficient, and will be expected to combine deep learning, geometric methods, and multimodal reasoning to build robust, production-grade systems.

Location – Mumbai / Bangalore / Hyderabad / Gurgaon (Hybrid – 3 days per week in office)

Responsibilities:

• Design and deploy computer vision systems for tasks such as:
• Object detection, segmentation, and tracking
• Scene understanding and structured perception
• Video understanding and temporal reasoning
• Build and optimize models using architectures such as:
• CNNs (ResNet, EfficientNet)
• Vision Transformers (ViT, Swin, DeiT)
• Detection/segmentation models (YOLO, DETR, Mask R-CNN)
• Develop multimodal systems combining vision and language:
• CLIP-style models
• Vision-language models (VLMs)
• Visual grounding and captioning systems
• Implement algorithms for:
• Multi-object tracking (SORT, DeepSORT, ByteTrack)
• Feature matching and representation learning
• Temporal modeling (RNNs, Transformers for video)
• Apply geometric and classical computer vision methods where relevant:
• Camera calibration
• Epipolar geometry
• Pose estimation
• 3D reconstruction or depth estimation
• Optimize systems for:
• Low-latency, real-time inference
• Throughput and scalability
• Edge and distributed deployment
• Design and build data pipelines for:
• Annotation workflows
• Dataset curation
• Synthetic data generation
• Integrate vision systems into:
• Multimodal AI pipelines
• Agent-based systems
• Decision-making workflows

Requirements:

• 5+ years of experience building computer vision systems in production environments
• Strong experience with deep learning frameworks (PyTorch / TensorFlow)
• Hands-on experience with:
• Detection, segmentation, or tracking systems
• Model training, fine-tuning, and evaluation
• Strong understanding of:
• Representation learning
• Loss functions (contrastive loss, focal loss, etc.)
• Evaluation metrics (mAP, IoU, precision/recall)
• Experience building and deploying end-to-end vision systems, not just training models

Candidates whose primary experience is limited to academic projects or model experimentation without real-world deployment may not be a fit for this role.

Nice to Have:

• Experience with multimodal systems (vision + language)
• Familiarity with models such as:
• CLIP, BLIP, Flamingo, or similar
• Experience with 3D vision:
• NeRFs
• SLAM
• Point clouds
• Experience with video understanding:
• Action recognition
• Event detection
• Experience building data engines:
• Active learning
• Hard negative mining
• Experience working with large-scale datasets and distributed training pipelines

Position: Computer Vision Engineer

Experience: 2–3 Years

Location: Bengaluru, Karnataka

Employment Type: Full-time

About the Role

We are seeking a highly motivated Computer Vision Engineer to join our autonomy and avionics team. The role involves developing, implementing, and validating computer vision models and algorithms and pipelines for UAVs operating in both GNSS-available and GNSS-denied environments.

The ideal candidate should have a strong foundation in theory of deep learning and machine learning, strong understanding of electromagnetic spectrum, imaging fundamentals, camera principles, and mathematical concepts with hands-on experience in implementing these algorithms on embedded or real-time systems.

Key Responsibilities

• Design, develop, and optimise AI Models
• Make custom CNNs/ modify existing CNNs to suit specific problems at hand
• Handle end-to-end training flow
• Implement end to end inference pipelines on standard PCs as well as on embedded systems
• Understand performance benchmarks and assess the accuracy and inference times
• Implement traditional image processing algorithms
• Factor the code to leverage underlying hardware architecture
• Prune the networks for efficiency
• Integrate the system within the application framework using C++
• Work closely with perception, controls, embedded software, and systems engineering teams.

Required Qualifications

• B.E./B.Tech/M.E./M.Tech in Computer Science and Engineering, Electronics, ECE, Mechatronics, or a related discipline.
• 2–3 years of experience in relevant area
• Strong understanding of: Linear Algebra, Probability and Statistics, AI-ML-DL fundamentals, Image processing, Camera Functioning
• Strong programming skills in C++ and Python.
• Experience with MATLAB for algorithm development and validation.
• Familiarity with Linux development environments.
• Experience with Git version control.

Preferred Skills

• Experience with Camera, IMU Calibration and Synchronisation
• Experience with multi-sensor fusion.
• Experience working with NVIDIA devices
• Experience on FPGA will be an added advantage
• Full understanding of Git functionality
• Exposure to airborne software development processes and coding standards (e.g., MISRA C++).

Personal Attributes

• Strong analytical and problem-solving skills.
• Ability to work independently on challenging technical problems.
• Good communication and documentation skills.
• Passion for solving challenging problems
• Willingness to participate in field trials and flight testing.
• Team player

About Naicos

Naicos, a fast-paced startup, builds AI-native products for algorithmic commerce: the future of how e-commerce runs. Our first products are already live with paying customers, and we are shipping new ones continuously.

Your Role

You will drive the research behind our imaging products, finding approaches to hard, unsolved problems in product and apparel imagery that work at production scale. You will run the experiments, prove what is viable, and hand a working approach to the engineering team.

Who We Are Looking For

•     Total experience: 3 years or more, with a strong research orientation

•     Deep learning frameworks in Python: PyTorch or TensorFlow

•     Image processing in Python: OpenCV, Pillow, scikit-image

•     Working knowledge of diffusion and other image generation models

We are looking for a strong research or research-student profile: someone who investigates, experiments and proves an approach, working closely with the AI Architect. Someone who is driven to build solutions, not just desk research.

AI Skills and Experience

•     Computer vision: classical CV alongside deep learning.

•     Segmentation, image-to-image translation, geometry and lighting; 

•     Generative imaging: diffusion models, conditioning and control, fine-tuning and LoRA,

•     Reads academic papers, judges what is reproducible, and turns one into a working prototype in days

Good to have

•     3D and rendering; published research or open-source contributions; model optimisation for inference cost

Research and innovative problem solving

•     Comfortable where there is no known answer, and defines the approach yourself

•     Solves problems inventively rather than reaching for the biggest model; many results come from classical image processing, fitment and geometric transformation

Other Relevant Skills and Experience

•     Designs experiments: baselines, measurable success criteria, honest reporting of negative results

•     Explains findings to a non-research audience and guides engineers to production

•     Git and reproducible experiment tracking (Weights & Biases, MLflow or similar)

Educational Qualification

•     BE / B.Tech / ME / M.Tech in Computer Science

•     BE / B.Tech / ME / M.Tech in any discipline with proven Computer Vision coursework or work

•     MSc / MS in Computer Science, Maths, Statistics or Computer Vision

•     PhD in Computer Vision or Machine Learning: an advantage, not a requirement

•     Reputed Tier 1 university preferred

GEMBA CONCEPTS

Experience: ~3–5 years Type: Full-time

AI/ML Engineer

Location: Bengaluru, India (Hybrid)

About Gemba Concepts

Gemba Concepts is a lean manufacturing and technology consulting firm helping clients across pharma, manufacturing, and logistics

modernize how they operate. We build production systems that sit close to the shop floor — warehouse management, manufacturing

traceability, and an applied AI/ML platform whose flagship use cases are visual quality inspection and predictive maintenance. We’re a

tight engineering team that ships real systems for demanding, often regulated, environments.

The Role

We’re looking for an AI/ML Engineer to take ML capabilities from prototype to production. You’ll own models end-to-end — framing the

problem with stakeholders, building and validating the model, and deploying it as a reliable service that holds up against real-world, messy

industrial data. This is a hands-on building role, not a pure research seat: your work goes into client-facing systems.

What You’ll Do

Build and ship computer vision models for visual quality inspection (defect detection, classification, segmentation) that perform under

real factory lighting, throughput, and edge-case conditions.

Develop predictive maintenance models using sensor/time-series data — anomaly detection, remaining-useful-life estimation, failure

prediction.

Own the full ML lifecycle: data pipelines, feature engineering, training, evaluation, and deployment, with proper versioning and monitoring.

Deploy and serve models in production on Azure (AKS), and keep them healthy — track drift, retraining triggers, and latency.

Integrate LLM-based capabilities (we use the Claude API and self-hosted open models) into delivery and product workflows where they

add leverage.

Collaborate with product, engineering, and domain experts to translate fuzzy operational problems into well-scoped ML solutions — and to

know when ML is not the right answer.

Communicate results and limitations clearly to non-ML stakeholders, including clients.

What We’re Looking For

3–5 years of hands-on experience building and deploying ML models in production (not just notebooks or coursework).

Strong Python and the modern ML stack — PyTorch or TensorFlow, scikit-learn, NumPy/Pandas.

Solid grounding in at least one of: computer vision (CNNs, object detection/segmentation, image preprocessing) or time-series /

anomaly detection.

Practical MLOps experience: containerization (Docker), model serving, experiment tracking, and deploying on a cloud platform — Azure /

Kubernetes (AKS) is a strong plus.

Comfort working with imperfect, real-world data — labeling strategy, class imbalance, data drift, and validation that reflects production

reality.

Good engineering hygiene (Git, testing, code review) and the ability to write code others can build on.

Nice to Have

Experience with industrial / manufacturing data or regulated environments (pharma, 21 CFR Part 11 awareness).

Hands-on LLM integration experience — RAG, prompt engineering, working with APIs or self-hosted models (vLLM, Qwen, etc.).

Edge deployment experience (running CV models on-device / near the line).

Exposure to data pipeline tooling and orchestration.

What You’ll Get

Real ownership of ML systems that go into production for serious clients.

A lean, senior-heavy team where you ship fast and learn across the stack.

Direct exposure to applied AI in manufacturing — a domain where the work has tangible, physical impact

Position Title: Real-Time Computer Vision & Edge AI Engineer (Founding Engineering Team / Core LLD) 

Reporting Structure: High-Level AI Architect (Principal ML Scientist, Google) 

Domain: Sub-16ms Edge AI, 3D Pose & Shape Estimation (SMPL-X), TensorRT C++ Inference, Zero-Copy Systems 

Performance Benchmark: Hard locked 60 FPS (<16.6 ms total frame budget) on dedicated RTX hardware

1. Position Overview & Architecture 

We are building a proprietary, ultra-low-latency spatial computing platform centered on high-fidelity 100% 3D Digital Twin architecture and real-time human digitization. 

In this role, you will serve as the Low-Level Design (LLD) Core AI Engineer, working directly alongside a Lead AI Scientist from Google. Your primary mandate is to solve complex surface occlusion and volumetric estimation challenges by building an ultra-fast C++ inference pipeline. This system must accurately regress a subject's true underlying 3D body shape and skeletal pose directly from a live camera feed. You will deploy models that extract parametric data (SMPL-X shape/pose parameters) and bridge these joint rotations seamlessly into our Vulkan graphics engine via shared GPU memory.

System Architecture: 

● Hardware Camera Ingestion: (V4L2 / GStreamer / CUDA) 

↓ Raw RGB Frames (Zero CPU Copy) 

● Edge AI Inference: (TensorRT / ONNX C++ API for 3D Pose Tracking, Kinematic Anchoring, SMPL-X Shape) 

↓ 3D Skeletal Transforms & Shape Parameters 

● Zero-Copy Shared Memory: (CUDA-Vulkan Bridge feeding directly into OpenRigLogic / MetaHuman Engine) 

2. Key Responsibilities & Deliverables 

A. Real-Time 3D Pose & Shape Estimation 

● Deploy and optimize state-of-the-art 3D human body reconstruction models (e.g., Shapy, SMPLify-X, CLIFF) to accurately regress the user's underlying skeletal structure and body volume, effectively bypassing unpredictable surface topologies and complex environmental occlusions. 

● Extract mathematically stable shape parameters (β) and pose parameters (θ) to drive the skeletal hierarchy of a high-fidelity digital avatar. 

B. Edge Inference Pipeline (TensorRT) 

● Translate Python-based research models into production-grade C++ inference engines using NVIDIA TensorRT and ONNX Runtime. 

● Implement INT8/FP16 quantization, layer fusion, and custom CUDA plugins to ensure the entire AI inference pass executes within a strict <10 ms budget per frame. 

C. Temporal Smoothing & Anti-Jitter Kinematics

● Implement highly optimized temporal filters (Kalman filters, One-Euro filters, optical flow tracking) in native C++ to eliminate all high-frequency jitter from the output joint rotations before they reach the graphics engine. 

● Ensure kinematic constraints (e.g., fixed bone lengths) are strictly maintained to prevent the digital asset from stretching or warping dynamically. 

D. Zero-Copy Ingestion & Engine Synchronization 

● Build hardware-accelerated video capture pipelines using V4L2 or GStreamer to ingest raw camera frames directly into GPU memory. 

● Bridge the output coordinate data and transformation matrices to the graphics team using POSIX shared memory and CUDA-Vulkan interop (VK_KHR_external_memory_fd), eliminating CPU staging overhead. 

3. Technical Qualifications & Tech Stack 

● Core Programming: Production-level Modern C++ (C++17/20), Python (strictly for model training/validation), and CUDA C/C++. 

● AI & Acceleration Frameworks: NVIDIA TensorRT, ONNX Runtime (C++ API), PyTorch. 

● Computer Vision Libraries: OpenCV (CUDA backend), MediaPipe C++ bindings. 

● Mathematical Foundations: 3D Kinematics, Matrix Transformations, Quaternions/Euler angles, statistical body modeling (SMPL/SMPL-X architecture). 

● Systems Architecture: Low-latency memory management, multi-threading (std::jthread, lock-free queues), SIMD vectorization. 

4. Relevant Projects & Demonstrable Experience (Preferred) 

Candidates will be preferred if they present functional codebases, GitHub repositories, or thesis work covering:

● Real-Time Body Fitting / Pose Estimation: Practical experience deploying 3D human pose or shape reconstruction models on live video feeds. 

● TensorRT / C++ Deployment: Demonstrable experience stripping a PyTorch model out of Python and running it natively in C++ using TensorRT or ONNX, ideally with custom CUDA layers or INT8 calibration. 

● High-Throughput Vision Pipelines: Built a C++ video processing pipeline that aggressively minimizes latency and avoids memory garbage collection pauses. 

● Kinematics & Smoothing: Applied mathematical filters to raw sensor or AI data to produce smooth, mechanically accurate 3D rotations. 

5. Compensation & Engagement Structure 

● Compensation: ₹1,50,000 to ₹2,00,000/month 

● Mentorship: Direct architectural guidance, algorithm review, and technical leadership from a Principal ML Scientist at Google. 

● Hardware: Dedicated high-end workstation equipped with discrete NVIDIA RTX hardware.

Hi,

Greetings !!

We/re are looking for someone who has Hands-on experience with CV/ML

The location for the same is Bangalore.

Requirements

• 11–14 years total experience
• Computer Vision – strong hands-on experience
• Object Detection – YOLO(Preferred), Faster R-CNN, SSD, etc.
• Image Processing – OpenCV, image enhancement, segmentation, feature extraction
• Machine Learning / Deep Learning – CNNs, model training, evaluation, optimization
• AI/ML – production-level AI solution development
• LLM / GenAI – practical exposure to LLMs, multimodal AI, RAG, VLMs, or GenAI
• Python – strong programming skills
• Model deployment – preferably TensorRT, ONNX, Docker, Kubernetes, cloud, or edge deployment
• Bangalore – candidate should be based in / willing to work from Bangalore

Preferred

• Vision Transformers / ViT
• YOLOv8/YOLOv9/YOLOv10/YOLO11
• PyTorch / TensorFlow
• NLP / LLM / VLM
• Generative AI
• CUDA / GPU optimization
• Edge AI / NVIDIA
• Experience leading CV/AI projects or teams

If interested, Share CV at: snigdhaattheratebeanhr.com

Key Responsibilities:

• Develop and deploy machine learning, deep learning, and NLP models for various business use cases.
• Build end-to-end ML pipelines including data preprocessing, feature engineering, training, evaluation, and production deployment.
• Optimize model performance and ensure scalability in production environments.
• Work closely with data scientists, product teams, and engineers to translate business requirements into AI solutions.
• Conduct data analysis to identify trends and insights.
• Implement MLOps practices for versioning, monitoring, and automating ML workflows.
• Research and evaluate new AI/ML techniques, tools, and frameworks.
• Document system architecture, model design, and development processes.

Required Skills:

• Strong programming skills in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras).
• Hands-on experience in building and deploying, finetuning ML/DL models in production.
• Good understanding of machine learning algorithms, neural networks, NLP, and computer vision.
• Experience with REST APIs, Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).
• Working knowledge of MLOps tools such as MLflow, Airflow, DVC, or Kubeflow.
• Familiarity with data pipelines and big data technologies (Spark, Hadoop) is a plus.
• Strong analytical skills and ability to work with large datasets.
• Excellent communication and problem-solving abilities.
• Experience in deploying models using cloud services (AWS Sagemaker, GCP Vertex AI, etc.).
• Experience in LLM fine-tuning or Generative AI, Voice AI, is an added advantage.

Educational Qualification:

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Machine Learning, IT, from IIT/NIT colleges strongly preferred

Job Summary

We are looking for an experienced AI/ML Engineer to design, develop, deploy, and maintain machine learning and AI solutions that address complex business problems. The ideal candidate should have strong hands-on experience in Python, Machine Learning, Generative AI, LLMs, and AI/ML deployment, with the ability to work across the complete AI/ML lifecycle.

Key Responsibilities

• Design, develop, and deploy scalable Machine Learning and AI models for real-world business use cases.
• Build and optimize ML pipelines covering data preparation, feature engineering, model development, evaluation, and deployment.
• Develop solutions using Generative AI, Large Language Models (LLMs), NLP, and deep learning.
• Work with LLMs, prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation (RAG) architectures.
• Integrate AI/ML models with enterprise applications and APIs.
• Fine-tune and evaluate ML/LLM models based on business requirements.
• Implement MLOps practices for model versioning, deployment, monitoring, and continuous improvement.
• Collaborate with Data Scientists, Software Engineers, Architects, Product Managers, and business stakeholders.
• Conduct model performance evaluation, optimization, and troubleshooting.
• Ensure AI solutions meet requirements around security, scalability, reliability, responsible AI, and data privacy.
• Stay current with emerging AI/ML technologies, frameworks, and industry best practices.

Required Skills

• Strong programming experience in Python.
• Strong understanding of Machine Learning algorithms, statistics, and data structures.
• Hands-on experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or equivalent.
• Experience with Generative AI and LLMs such as OpenAI, Azure OpenAI, Claude, Gemini, or open-source models.
• Strong knowledge of Prompt Engineering, RAG, embeddings, vector databases, and AI agents.
• Experience with NLP, deep learning, or computer vision is an advantage.
• Experience developing and consuming REST APIs and microservices.
• Working knowledge of SQL and NoSQL databases.
• Experience with cloud platforms such as Azure, AWS, or GCP.
• Understanding of Docker, Kubernetes, CI/CD, and MLOps.
• Familiarity with Git and modern software development practices.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• 4 years of relevant experience in AI/ML engineering or a related field.
• Experience building and deploying production-grade AI/ML solutions.
• Enterprise application development experience.
• Experience with Azure OpenAI, AWS Bedrock, Vertex AI, or similar managed AI platforms.
• Experience with LangChain, LlamaIndex, Semantic Kernel, or comparable AI frameworks is a plus.
• Experience with AI/ML model monitoring, evaluation, and optimization.

What You Bring

• Strong problem-solving and analytical skills.
• Ability to translate business requirements into practical AI/ML solutions.
• Strong software engineering and debugging capabilities.
• Ability to work independently as well as collaboratively in a cross-functional environment.
• Good communication skills with the ability to explain complex AI concepts to technical and non-technical stakeholders.

Keywords

AI Engineer | ML Engineer | Machine Learning | Generative AI | LLM | Python | NLP | Deep Learning | RAG | Prompt Engineering | AI Agents | Azure OpenAI | AWS Bedrock | MLOps | TensorFlow | PyTorch | Scikit-learn | Vector Database | Cloud AI

Location: Gurugram

Work mode: Hybrid

Embedded Systems / Firmware Engineer

Key Responsibilities

• Develop embedded firmware for IoT and connected devices using C/C++.
• Work on RTOS, bare-metal programming, memory management, device drivers, and communication protocols.
• Develop and integrate drivers/interfaces for cameras, motors, sensors, and vision systems.
• Work with Python and Shell scripting for development and automation.
• Apply basic AI/ML and computer vision concepts to embedded applications.
• Collaborate on electrical and mechanical design, hardware integration, testing, and debugging.

Required Skills

• C, C++, Python, Shell Scripting
• Embedded Systems & Firmware
• IoT, RTOS, Bare Metal
• Memory Management & Device Drivers
• Communication Protocols
• Camera, Motor & Vision Systems
• Basic AI/ML understanding
• Electrical & Mechanical Design understanding

About us

MyRico builds personal AI agents for enterprise, the copilots and digital employees that make humans more productive. The MyRico agents sit at the intersection of enterprise memory, high-end security, and an ever-expanding set of capabilities. We're a small team shipping fast, and the product is live with real customers today.

The role

Full-time · Bangalore

You'll own the systems that make an autonomous agent trustworthy in production. This is not just prompt engineering, and it's not model training - it's that and all the engineering layers in between: model steering, memory architecture, orchestration design, latency optimization, deterministic vs non-deterministic systems tradeoff, deployment, and operator tooling.

Concretely, the kind of work you'd have done here last week would include enhancing agent memory systems, runtime and scheduling reliability and predictability, adding new capability and tools to deployed agents, operator tools, live system debugging and benchmarking various models for price, latency and quality. And that is just last week. We are a small nimble startup rapidly working to address customer needs in this growing space, so things change rapidly.

What we're looking for

• More than 7 years of software engineering, with real production ownership of distributed or stateful systems - you've been paged for something you built and made it not happen again.
• Strong understanding of LLM based native app building, combining classic and model driven applications to get the best of both. You've built on LLMs beyond demos: agent frameworks, tool use, context management, eval fixtures, and you know why "it worked in the transcript" isn't evidence.
• Python and shell in production settings; comfortable in TypeScript/Node. You write boring, testable code and prefer the standard library to a new dependency.
• Systems taste: append-only logs, idempotent reconciliation, fold-the-events state machines, and read-only debugging surfaces feel like home.
• Evidence discipline: tests before features, claims backed by quoted observations, decisions written down.

Nice to have

• Experience running the combination of multi-tenant and single-tenant / on-prem-style fleets with ability to handle per-customer isolation, upgrade paths, migration compatibility in both setups.
• Security instincts for products that touch highly sensitive data and systems, including things like executives' email, calendars, and messages: least privilege, loopback-only services, secrets that never hit a log.
• You already orchestrate AI coding agents in your own workflow and have opinions about where they break.

How we work

Small team, high trust, written decisions. Designs get adversarial review before code; PRs get automated review driven to zero open findings; features aren't done until verified on a live system. AI agents do a large share of the implementation, your leverage is judgment: framing the problem, freezing the right design, and knowing when the machine is wrong.