AI Developer at Codezen Tech Solutions · Mumbai, Navi Mumbai, Raipur · 2 - 3 years · ₹8L - ₹12L / yr · Profitable · Posted 10 Jun 2025
We’re looking for an experienced AI Developer to join our team and drive the design, development, and deployment of advanced computer-vision and LLM-based solutions. You’ll build real-time object detection & tracking pipelines (LLM, YOLOv8/11), develop face-recognition models on video feeds with LLM support, and manage GPU-powered server infrastructure end-to-end.
Key Responsibilities
- Model Development & Optimization
- Design, train, and fine-tune LLMs, YOLOv8/11 models for multi-class object detection.
- Implement face-recognition pipelines leveraging state-of-the-art LLMs and embedding techniques.
- Optimize inference speed and accuracy for real-time video processing on GPUs.
- Video Analytics & Tracking
- Develop multi-camera object-tracking algorithms across disparate video sources.
- Integrate OpenCV, DeepSORT (or similar), and custom tracking logic for robust performance.
- Infrastructure & Deployment
- Provision and maintain Linux servers with SSH, Docker, and GPU drivers (NVIDIA CUDA/cuDNN).
- Automate CI/CD pipelines for model training, evaluation, and deployment.
- Monitor GPU utilization, troubleshoot performance bottlenecks, and ensure high availability.
- Collaboration & Documentation
- Work closely with product managers, data engineers, and front-end teams to integrate AI services via REST/gRPC APIs.
- Write clear technical documentation, API specs, and best-practice guides.
Required Qualifications
- Bachelor’s or Master’s in Computer Science, Electrical Engineering, or related field.
- 2-3+ years hands-on experience with:
- YOLOv8 or YOLOv11 (training, inference, transfer learning)
- Face recognition frameworks (InsightFace, FaceNet, ArcFace) and LLM embeddings (e.g., Hugging Face Transformers)
- Python ecosystem: PyTorch/TensorFlow, OpenCV, NumPy, scikit-learn
- Strong Linux skills: server provisioning, SSH, shell scripting.
- Proficiency in managing GPU servers (NVIDIA CUDA, Docker GPU containers)
- Experience deploying models in production (AWS/GCP/Azure or on-prem clusters).
Preferred Qualifications
- Familiarity with video-streaming protocols (RTSP/WebRTC).
- Knowledge of microservices architecture and API gateways.
- Exposure to MLOps platforms (MLflow, Kubeflow).
- Strong debugging, profiling, and performance-tuning abilities.
Shubham Vishwakarma
Full Stack Developer - Averlon
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Machine Learning (ML)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
Python
Deep LearningMachine Learning (ML)
C++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.
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
Machine Learning (ML)PythonAbout the role
We are building AI systems that read, understand and act on real business documents, bank statements, financial reports, policy documents and forms and putting them into production where accuracy and cost both matters.
This is not a research role and it is not a prompt-writing role. You will own features end to end: pick and deploy open-source models, build the pipelines around them, measure whether they actually work on our documents, drive the cost per document down, and keep the whole thing running in production.
You will work closely with the engineering and product teams, and your work will be directly used by business users from day one.
What you will do
Deploy and evaluate open-source models
- Select, deploy and benchmark open-source LLMs and vision-language models for specific, narrow use cases not general chat.
- Build evaluation sets from real documents and define what "good" means numerically (field-level accuracy, extraction recall, hallucination rate) before shipping.
- Run structured comparisons between models and approaches, and write up the trade-offs so the team can make a decision.
- Apply quantization, batching and other optimizations to fit models into a sensible GPU budget.
Build and optimize AI orchestration
- Design multi-step pipelines that combine deterministic code, ML models and LLM calls and know when not to use an LLM.
- Optimize for latency, cost and reliability: caching, batching, request routing, fallback tiers, retries and graceful degradation.
- Instrument pipelines so failures are visible and traceable rather than silent.
Ship to production
- Package models and services with Docker, expose them behind clean APIs, and deploy them to our GPU and CPU infrastructure.
- Handle the unglamorous production concerns: cold starts, timeouts, concurrency limits, versioning, rollback and monitoring.
- Own on-call-style responsibility for the AI features you build, including cost tracking.
Must-have skills
Programming & engineering
- Strong Python: type hints, async/await, dataclasses/Pydantic, clean module design, testing.
- REST API development with FastAPI (or Flask/Django with a willingness to move to FastAPI).
- Git, code review discipline, and the ability to write code someone else can maintain.
- Comfortable in Linux and on the command line.
Machine learning fundamentals
- Working knowledge of PyTorch and the Hugging Face ecosystem (transformers, tokenizers, accelerate).
- Understanding of inference-time concepts: tokenization, context windows, batching, precision (FP16/BF16/INT8), memory footprint.
- Ability to read a model card and a paper well enough to judge whether a model fits a use case.
Document processing
- Hands-on experience with at least two of: pypdfium2, PyMuPDF, pdfplumber, pdfminer.six, Docling, Unstructured, Surya, DocTR, LayoutLM family.
- Practical OCR experience (Tesseract, PaddleOCR, or a cloud OCR) and an understanding of when OCR is the wrong tool.
- Experience extracting tables from PDFs and dealing with merged cells, multi-line rows, and inconsistent column layouts.
Strongly preferred
You will be a much stronger candidate with any of these. We do not expect all of them.
Model serving & optimization
- vLLM, TGI, Ollama, llama.cpp, or Triton Inference Server.
- Quantization formats and tooling: GGUF, AWQ, GPTQ, bitsandbytes, ONNX Runtime, INT8 export.
- Serverless GPU platforms: Modal, RunPod, Replicate, Baseten including cold-start and container-lifecycle management.
- LoRA / QLoRA fine-tuning with PEFT for narrow, task-specific improvements.
Vision-language models
- Practical use of open VLMs: Qwen2.5-VL, InternVL, Granite Vision, Molmo, Phi-Vision, or similar.
- Awareness of where VLMs hallucinate especially on numeric and financial content and patterns for constraining them (using the model for layout only, sourcing values from the text layer, constrained decoding).
Orchestration & pipelines
- Workflow orchestration: Dagster, Airflow, Prefect, or Temporal.
- Async job patterns: Celery, RQ, or platform-native spawn/poll patterns.
- LLM orchestration frameworks (LangGraph, LlamaIndex, Haystack) with the judgement to know when plain Python is a better answer.
- Structured output enforcement: Instructor, Outlines, XGrammar, JSON schema / tool-use modes.
Evaluation & observability
- Building golden datasets and regression suites for extraction tasks.
- Eval tooling: promptfoo, DeepEval, Ragas, or in-house harnesses.
- LLM tracing and monitoring: Langfuse, Arize Phoenix, LangSmith, OpenTelemetry.
Nice extras
- Rule engines and policy evaluation (Open Policy Agent / Rego, Drools, rule-engine).
- Experience in fintech, lending, insurance or accounting documents.
- Handling of PII and data-security practices in document pipelines.
- Contributions to open-source ML or document-processing projects.
Why join us
- Real production ownership from month one your work goes to actual users, not a demo.
- Genuinely hard technical problems in document AI, not wrappers over an API.
- Small team, short decision cycles, direct access to leadership.
- Budget and freedom to evaluate and adopt new open-source models as they land.
To apply: send your CV along with a short note on one AI system you have taken to production what it did, what the accuracy was, and what broke.
Python
Docker
KubernetesHiring for AI Engineer
Exp: 6 - 8 yrs
Edu : BE/B.Tech/MCA
Work Location : Pune
Skill Set:
- Total experience ranging from 6–8 years in software engineering/AI roles
- Min 5 years strong programming experience in Python is a MUST
- Min 3.5 years hands-on experience in AI with LLMs, RAG pipelines, and AI frameworks
- Experience with cloud platforms (AWS/Azure/GCP)
Role: AI Developer
Experience: 3–4 Years
Employment Type: Full-Time
Location: Goregaon, Mumbai
About the Role
We are looking for an experienced AI Developer with 3–4 years of software development experience and strong hands-on exposure to Generative AI, AI Agents, Copilots, and AI-powered application development.
The candidate will be responsible for building production-ready AI solutions, developing agentic workflows, modernizing legacy applications, and integrating LLM capabilities into enterprise applications.
Key Responsibilities
- Design, develop, and deploy AI Agents and agentic workflows for enterprise use cases.
- Build AI Copilots and LLM-powered applications using modern AI frameworks and APIs.
- Develop RAG-based applications using embeddings, vector databases, and enterprise data.
- Work on legacy application migration and modernization, leveraging AI-assisted development and code transformation techniques.
- Analyze legacy codebases and design strategies for AI-driven migration, refactoring, and modernization.
- Integrate LLMs with enterprise applications, APIs, databases, and third-party systems.
- Implement tool calling, function calling, multi-agent workflows, and workflow automation.
- Perform prompt engineering, context optimization, model evaluation, and AI application testing.
- Take ownership of AI solutions from POC and prototyping through production deployment.
- Collaborate with product managers, architects, and engineering teams to convert business requirements into scalable AI solutions.
- Stay updated with emerging technologies in Generative AI, Agentic AI, LLMs, and AI-assisted software development.
Required Skills
- 3–4 years of professional software development experience.
- Strong proficiency in Python and/or JavaScript/TypeScript.
- Hands-on experience developing Generative AI / LLM-based applications.
- Strong understanding of AI Agents, RAG, Prompt Engineering, LLM APIs, and embeddings.
- Experience with frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent.
- Experience working with REST APIs, databases, Git, and cloud environments.
- Hands-on experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, or equivalent.
- Good understanding of software architecture, debugging, testing, and deployment practices.
Good to Have
- Experience with Microsoft Copilot / Copilot Studio.
- Experience working with Claude, OpenAI, Gemini, Azure OpenAI, or open-source LLMs.
- Experience in legacy application migration, modernization, or code conversion.
- Knowledge of Azure AI / AWS / Google Cloud AI services.
- Experience with MCP, multi-agent systems, tool calling, and AI orchestration.
- Experience building enterprise-grade AI solutions with focus on security, scalability, and performance.
C++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
PythonMachine Learning (ML)Job Summary/ Job Opportunity:
This is an excellent opportunity for an ideal candidate with a high level of technical proficiency and meeting the below mentioned criteria -- • Strong experience in Machine Learning, Deep Learning, Generative AI, and Large Language Models (LLMs). • Hands-on experience building and deploying production-grade solutions using Azure OpenAI, OpenAI, LangChain, LangGraph, Semantic Kernel, LlamaIndex, and Agentic AI frameworks. • Strong expertise in Python, API development, microservices, and cloud-native architectures. • Experience designing and implementing RAG solutions, vector databases, embeddings, knowledge retrieval systems, and AI copilots. • Experience with Azure cloud services, MLOps, CI/CD pipelines, monitoring, and model lifecycle management. • Strong understanding of AI governance, responsible AI, security, compliance, and model evaluation frameworks. • Ability to lead technical discussions, provide architectural recommendations, mentor team members, and interact with business stakeholde
Key Objectives and Major Responsibilities:
• Design, develop, and implement scalable AI/ML and Generative AI solutions for enterprise applications. • Lead development of intelligent applications leveraging LLMs, RAG pipelines, AI agents, and document intelligence solutions. • Collaborate with business stakeholders, architects, and product teams to translate business requirements into technical solutions. • Design and optimize data pipelines, vector search solutions, embeddings, and retrieval mechanisms. • Build and maintain REST APIs, microservices, and cloud-native AI applications. • Ensure best practices in coding standards, performance optimization, security, scalability, and maintainability. • Drive AI solution deployment using MLOps practices, CI/CD pipelines, monitoring, and observability frameworks. • Perform code reviews, mentor junior developers, and contribute to capability building within the team
Key Capabilities and Competencies:
Knowledge, Skills, Qualification and Experience
• Degree in B.Tech/M.Tech (Computer Science/IT/Data Science) or related discipline preferred, with 3–4 years of relevant experience in AI/ML, GenAI and total 5-7 years of experience. • Proficiency in Python and hands-on experience with ML libraries (scikit-learn, TensorFlow, PyTorch) and GenAI frameworks/tools. • Strong understanding of machine learning, deep learning, LLMs, prompt engineering, and techniques like RAG and fine-tuning. • Experience with data processing, embeddings, vector databases, APIs, and building scalable AI driven applications. • Good communication skills, ability to work on multiple projects, and eagerness to learn and adapt to evolving AI technologies.
PythonMachine Learning (ML)We are building an advanced, AI-driven multi-agent software system designed to revolutionize task automation and code generation. This is a futuristic AI platform capable of:
✅ Real-time self-coding based on tasks
✅ Autonomous multi-agent collaboration
✅ AI-powered decision-making
✅ Cross-platform compatibility (Desktop, Web, Mobile)
We are hiring a highly skilled **AI Engineer & Full-Stack Developer** based in India, with a strong background in AI/ML, multi-agent architecture, and scalable, production-grade software development.
### Responsibilities:
- Build and maintain a multi-agent AI system (AutoGPT, BabyAGI, MetaGPT concepts)
- Integrate large language models (GPT-4o, Claude, open-source LLMs)
- Develop full-stack components (Backend: Python, FastAPI/Flask, Frontend: React/Next.js)
- Work on real-time task execution pipelines
- Build cross-platform apps using Electron or Flutter
- Implement Redis, Vector databases, scalable APIs
- Guide the architecture of autonomous, self-coding AI systems
### Must-Have Skills:
- Python (advanced, AI applications)
- AI/ML experience, including multi-agent orchestration
- LLM integration knowledge
- Full-stack development: React or Next.js
- Redis, Vector Databases (e.g., Pinecone, FAISS)
- Real-time applications (websockets, event-driven)
- Cloud deployment (AWS, GCP)
### Good to Have:
- Experience with code-generation AI models (Codex, GPT-4o coding abilities)
- Microservices and secure system design
- Knowledge of AI for workflow automation and productivity tools
Join us to work on cutting-edge AI technology that builds the future of autonomous software.
PythonMachine Learning (ML)Experience - 4 to 6 year
Location – Ahmedabad/Pune/Indore
- Additional Job Description
Additional Job Description
Required Skills and Experience:
- Strong proficiency in Python and experience with ML/AI libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face ecosystem).
- Hands-on experience with LLMs, RAG, vector databases, and retrieval pipelines.
- Practical experience deploying agentic workflows and building multi-step, tool-enabled agents.
- Experience using Garak (or similar LLM red-teaming/vulnerability scanners) to identify model weaknesses and harden deployments.
- Demonstrated experience implementing content filtering / moderation systems.
- Solid skills working with structured and unstructured data and advanced feature engineering.
- Familiarity with cloud GenAI platforms and services (Azure AI Services preferred; AWS/GCP acceptable).
- Experience building APIs/microservices; containerization (Docker), orchestration (Kubernetes).
- Strong understanding of model evaluation, performance profiling, inference cost optimization, and observability.
- Good knowledge of security, data governance, and privacy best practices for AI systems.
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon