computer vision engineer

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 playwe

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

AI based systems design and development, entire pipeline from image/ video ingest, metadata ingest, processing, encoding, transmitting.

Implementation and testing of advanced computer vision algorithms.

Dataset search, preparation, annotation, training, testing, fine tuning of vision CNN models. Multimodal AI, LLMs, hardware deployment, explainability.

Detailed analysis of results. Documentation, version control, client support, upgrades.

About the Role We are seeking a highly technical, hands-on Senior AI/ML Tech Lead to drive the design, development, and deployment of cutting-edge Generative AI applications. In this dual-impact role, you wi l act as a primary individual contributor architecting core AI engines while simultaneously leading a team of engineers through task alocation, code reviews, and technical mentorship. The ideal candidate bridges the gap between state-of-the-art AI research (LLMs, Agentic frameworks, Advanced RAG, OCR) and production-grade ful-stack engineering (Python, FastAPI, React).

Key Responsibilities

Technical Leadership & Team Management (40%)

● Technical Oversight: Lead a team of AI, backend, and ful-stack engineers; alocate tasks, establish sprint priorities, and ensure timely delivery.

● Code Quality & Reviews: Conduct rigorous code reviews to maintain high engineering standards, security, performance, and scalability across AI and fu l-stack codebases.

● Architecture & Governance: Design end-to-end system architectures for AI solutions, ensuring seamless integration between frontend interfaces, backend APIs, and AI models.

● Mentorship: Guide and upskil team members on modern software practices, LLM engineering, and agentic design patterns. Hands-On Engineering & Development (60%)

● Generative AI & Agentic Systems: Architect, build, and optimize LLM-powered applications, multi-agent workflows (e.g., CrewAI, AutoGen, LangGraph), and autonomous AI agents.

● RAG & OCR Pipelines: Design and deploy advanced RAG (Retrieval-Augmented Generation) architectures and document processing pipelines utilizing OCR techniques (e.g., LayoutLM, PaddleOCR, Tesseract, Vision LLMs) to extract structured data from unstructured sources.

● Backend Systems: Build robust, asynchronous, high-throughput microservices and RESTful APIs using Python and FastAPI.

● Frontend Integration: Colaborate on or build modern web interfaces using React (e.g., Control Towers, operations dashboards, interactive chat interfaces).

● MLOps & Vector DBs: Oversee model deployment, prompt engineering, fine-tuning, vector database integration (Pinecone, Qdrant, Chroma, PGVector), and cloud infrastructure setup (Azure/AWS).

Required Qualifications & Skills

● Overall Experience: 8 to 10 years of professional software engineering experience.

● AI/ML Domain Experience: 3 to 4+ years of dedicated, hands-on experience building and deploying AI/ML, OCR, and Generative AI solutions in production.

● Core Technical Stack: ○ Generative AI & LLMs: Extensive experience with commercial and open-source LLMs (OpenAI, Anthropic Claude, Llama), Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI), and LLM evaluation frameworks (LangSmith, TruLens, Ragas). ○ RAG & Unstructured Data: Strong knowledge of hybrid search, re-ranking, chunking strategies, vector databases, and document inte ligence workflows. ○ OCR & Vision Techniques: Hands-on experience with OCR engines (Tesseract, PaddleOCR, Azure Document Inteligence) and Multi-Modal/Vision LLMs for document extraction. ○ Backend: Deep expertise in Python and asynchronous frameworks (FastAPI, AsyncIO). ○ Frontend: Working proficiency in React (TypeScript/JavaScript) for building interactive web UI components. ○ Cloud & DevOps: Hands-on experience with cloud platforms (Azure / AWS), Docker, Kubernetes, and CI/CD pipelines.

Preferred / Good-to-Have Skills

● Experience with cloud-native data platforms (e.g., Microsoft Fabric, Snowflake, Azure SQL).

● Familiarity with cost optimization and latency reduction techniques for LLM inference (caching, semantic routing, model quantization).

● Prior experience in client-facing technical leadership or agile consulting environments.

What We Offer

● Opportunity to lead and build high-impact, state-of-the-art Generative AI systems.

● Colaborative engineering culture with room for technical ownership and direct business impact.

● Flexible work arrangements and competitive compensation package.

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

Role: Principal AI Architect — Multimodal Video Intelligence

Location: India Remote, with overlap with Singapore working hours

Employment Type: Full-time

Reporting to: Founder / CEO

Function: AI Architecture, Multimodal AI, Video Intelligence, Media Representation

About the Client

The client is building an AI-native media intelligence platform that transforms long-form video into structured, searchable, reusable and monetisable media intelligence.

The platform is not simply a video-clipping tool. We are developing a persistent intelligence layer for media, where video, audio, speech, text, objects, scenes, events, entities, emotions, narrative arcs and commercial signals are processed into a reusable representation that can support multiple downstream use cases, including:

• short-form clip generation;
• semantic search;
• scene and narrative understanding;
• contextual advertising;
• shoppable video;
• creator and content analytics;
• automated editing workflows;
• future media-intelligence APIs.

We are looking for a Principal AI Architect who can define and guide the AI architecture behind this platform.

Role Summary

The Principal AI Architect — Multimodal Video Intelligence will own the technical architecture for AI systems, including multimodal video understanding, persistent media representation, model orchestration, evaluation frameworks, and production AI design.

This is a hands-on architecture role. The ideal candidate can move between research papers, model selection, system design, data schemas, prototype review, engineering trade-offs, and implementation guidance.

You will work closely with the Founder / CEO, senior AI engineers, computer vision engineers, backend engineers and external vendors to convert the product and IP vision into a robust technical system.

Key Responsibilities

1. AI System Architecture

• Define the end-to-end AI architecture for long-form video understanding.
• Design the processing pipeline from video ingest to structured media intelligence.
• Define how vision, audio, speech, text, metadata and user signals should be fused.
• Design the architecture for reusable media intelligence rather than one-time clip generation.
• Ensure the system can support multiple downstream applications from the same processed media layer.

2. Persistent Media Representation

• Design persistent media representation layer across multiple levels, including frame, object, shot, scene, segment, entity, event and full-video levels.
• Define what intelligence must be stored permanently versus computed on demand.
• Design schemas for temporal, spatial, semantic, narrative and commercial metadata.
• Define provenance, confidence, model versioning and evidence-tracking requirements.
• Ensure the representation remains usable even when underlying AI models are replaced or upgraded.

3. Multimodal Model Strategy

• Select and evaluate appropriate models for video, image, audio, speech, OCR, entity extraction, scene understanding, action recognition, embeddings, reranking and LLM/VLM reasoning.
• Decide where to use open-source models, commercial APIs, fine-tuning or custom models.
• Define model interfaces so models can be swapped without breaking downstream systems.
• Guide model benchmarking for accuracy, latency, cost and scalability.
• Prevent over-dependence on any single model vendor or API.

4. Temporal and Narrative Intelligence

• Design approaches for understanding long-form video structure, including scenes, events, story arcs, character/entity continuity and engagement peaks.
• Define methods to identify clip-worthy moments across different content types.
• Support narrative scoring, highlight ranking, scene segmentation and coherence validation.
• Ensure that clips are not only visually interesting but contextually and narratively coherent.

5. Evaluation and Benchmarking

• Define objective evaluation frameworks for AI outputs.
• Build or guide creation of benchmark datasets and UAT criteria.
• Define metrics for clip quality, scene accuracy, entity continuity, timestamp alignment, hallucination control, ranking quality, retrieval precision and cost efficiency.
• Establish model and prompt evaluation processes.
• Create regression-testing methodology when models, prompts, schemas or scoring logic change.

6. Search, Retrieval and Knowledge Layer

• Design hybrid search architecture across transcript, visual events, metadata, embeddings and structured knowledge.
• Define when to use relational storage, vector databases, graph databases and object storage.
• Design queryable media intelligence for downstream APIs and applications.
• Support knowledge-graph or ontology-based representation where useful.
• Ensure retrieved outputs are evidence-backed and timestamp-grounded.

7. Production AI Architecture

• Work with AI engineers to convert architecture into deployable services.
• Guide decisions on batching, GPU inference, model serving, queues, retries, observability and cost controls.
• Review pipeline designs involving FFmpeg, GStreamer, DeepStream, TensorRT, Triton, ONNX, cloud services and model APIs.
• Define failure-handling, reprocessing, versioning and rollback mechanisms.
• Support scalable design without premature overengineering.

8. IP and Technical Differentiation

• Help translate AI architecture into defensible technical differentiation.
• Support patent-related technical disclosures where required.
• Identify what is proprietary versus commodity.
• Avoid building a generic wrapper over existing models.
• Ensure the architecture reinforces the core thesis of persistent, reusable media intelligence.

9. Team Guidance

• Provide technical direction to senior AI engineers and computer vision engineers.
• Review designs, experiments, evaluation results and architecture decisions.
• Mentor engineers without becoming a pure people manager.
• Help define technical milestones for the first 90, 180 and 365 days.
• Support hiring, technical interviews and vendor evaluation where needed.

Required Experience

The ideal candidate should have:

• 8+ years of AI/ML experience, with significant exposure to computer vision, video AI, multimodal AI, retrieval systems or production ML architecture.
• Strong experience designing AI systems, not only implementing isolated models.
• Hands-on experience with video understanding, temporal modelling, multimodal pipelines, VLMs, LLMs, embeddings, ranking or retrieval.
• Experience taking AI systems from prototype to production.
• Strong knowledge of Python and modern AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face or equivalent.
• Experience with model evaluation, benchmarking, error analysis and dataset design.
• Understanding of production architecture: APIs, queues, databases, cloud, model serving, observability and deployment trade-offs.
• Ability to work with founders and engineers in a high-ambiguity startup environment.

Strongly Preferred Experience

• Video understanding, action recognition, scene segmentation, event detection or video retrieval.
• Multimodal AI involving video, audio, speech, text and metadata.
• LLM/VLM orchestration for structured outputs.
• Prompt/version management, schema validation and hallucination control.
• Embedding search, vector databases, reranking and retrieval evaluation.
• Knowledge graphs, ontologies, entity resolution or temporal knowledge representation.
• Model serving using TensorRT, Triton, ONNX, vLLM, DeepStream or similar.
• Experience with long-form video, OTT, sports media, entertainment, creator platforms, advertising technology or social commerce.
• Experience contributing to patents, technical disclosures or investor diligence.

Technical Areas

The candidate should be comfortable discussing and making architecture decisions across:

• Computer vision;
• video AI;
• multimodal fusion;
• speech-to-text;
• OCR;
• image/video embeddings;
• VLMs and LLMs;
• semantic search;
• vector databases;
• graph databases;
• temporal reasoning;
• ranking and scoring systems;
• prompt orchestration;
• model evaluation;
• model versioning;
• data lineage;
• GPU inference;
• cloud AI deployment.

What This Role Is Not

This is not a role for someone who has only built:

• chatbots;
• basic RAG demos;
• LangChain prototypes;
• prompt-engineering workflows;
• simple OpenAI/Gemini API wrappers;
• dashboards over model outputs;
• classical computer vision demos without production architecture;
• MLOps pipelines without AI system-design depth.

The role requires architectural depth in AI systems, not just familiarity with AI tools.

First 90-Day Expectations

First 30 Days

• Review product thesis, patent direction, prototype plans and existing technical assumptions.
• Assess current team capability and architecture gaps.
• Define the first version of AI architecture.
• Identify immediate technical risks and validation priorities.

First 60 Days

• Deliver a detailed architecture document covering media representation, model stack, pipeline design, storage strategy, evaluation framework and implementation roadmap.
• Define the canonical media-intelligence schema.
• Define model-selection and benchmarking criteria.
• Guide senior engineers on first implementation milestones.

First 90 Days

• Help the team implement and validate the first working version of the persistent media-intelligence layer.
• Establish evaluation datasets and UAT metrics.
• Review prototype outputs and improve architecture based on evidence.
• Produce a 6-month AI roadmap with technical risks, milestones and resourcing needs.

Success Metrics

The Principal AI Architect will be successful if:

• They have a clear AI architecture that the engineering team can execute.
• The platform does not collapse into a generic clip-generation pipeline.
• The media representation is reusable across multiple use cases.
• Models, prompts and schemas are versioned and testable.
• AI outputs are measurable through objective benchmarks.
• Snehashish, Abhishek and other engineers have clear technical direction.
• The architecture supports both product execution and investor/IP defensibility.

Candidate Personality Fit

The right candidate should be:

• intellectually strong but practical;
• hands-on enough to review code and experiments;
• comfortable with ambiguity;
• willing to challenge assumptions with evidence;
• able to simplify complex AI architecture for engineers and investors;
• disciplined about evaluation, cost and production constraints;
• not attached to one model, tool or vendor;
• able to work in a founder-led early-stage startup.