[Software Services] : Sr. AI/ML Developer
vinove· Software Delivery
About this role
| ML & Generative AI Developer | Senior Individual Contributor | 6–8 Years Experience |
| Role Overview | We are looking for a seasoned ML & Generative AI Developer with 6–8 years of hands-on experience to join our AI/ML team. The ideal candidate will bring deep expertise across the full machine learning lifecycle — from model design and training to production deployment — alongside strong command of modern Generative AI technologies, including LLM fine-tuning, RAG pipelines, and autonomous agentic systems. |
| Department: | Artificial Intelligence & Machine Learning |
| Level: | Senior Individual Contributor |
| Experience: | 6–8 Years |
| Employment Type: | Full-time |
| Location: | In Office |
Key Responsibilities
Machine Learning Model Development
- Design, develop, and optimize end-to-end machine learning models for classification, regression, NLP, computer vision, and recommendation tasks.
- Conduct feature engineering, model selection, hyperparameter tuning, and performance evaluation using industry-standard frameworks.
- Ensure models meet production-grade accuracy, latency, and scalability requirements.
Training Pipelines & Data Infrastructure
- Architect and implement scalable ML training pipelines with robust data ingestion, preprocessing, augmentation, and validation stages.
- Design and manage distributed training workflows (single-node and multi-GPU/TPU clusters) using frameworks such as PyTorch, TensorFlow, and JAX.
- Build automated experiment tracking and reproducibility systems using tools like MLflow, Weights & Biases, or DVC.
MLOps & Production Engineering
- Deploy, monitor, and maintain ML models in production using containerized (Docker, Kubernetes) and cloud-native infrastructure.
- Establish CI/CD pipelines for model training, evaluation, versioning, and automated re-training triggers.
- Implement model performance monitoring, drift detection, and alerting systems to ensure sustained model health.
- Manage model registries and artifacts using platforms such as SageMaker, Vertex AI, Azure ML, or open-source MLOps stacks.
Generative AI Development
- Build and deploy production-grade Generative AI applications using leading platforms and open-source models.
- Proprietary: OpenAI GPT-4/o, Anthropic Claude, Google Gemini, Amazon Titan, Cohere
- Open-source: Meta LLaMA 2/3, Mistral, Falcon, Mixtral, Phi-3, Gemma
- Design prompting strategies including zero-shot, few-shot, chain-of-thought (CoT), and structured output prompting for diverse task types.
- Evaluate LLM outputs for hallucination, toxicity, relevance, and faithfulness using automated and human-in-the-loop evaluation frameworks.
RAG, Agentic AI & Fine-Tuning
- Design and implement Retrieval-Augmented Generation (RAG) pipelines with semantic chunking, hybrid search (dense + sparse), reranking, and document parsing strategies.
- Build and orchestrate Agentic AI systems with memory, planning, tool use, and multi-agent collaboration using LangChain, LangGraph, AutoGen, CrewAI, or similar frameworks.
- Apply parameter-efficient fine-tuning (PEFT) techniques, particularly Low-Rank Adaptation (LoRA) and QLoRA, for domain adaptation and instruction tuning of large language models.
- Implement Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) pipelines where applicable.
Required Qualifications
- 6–8 years of professional experience in machine learning and AI development.
- Strong proficiency in Python and ML libraries: NumPy, Pandas, Scikit-learn, PyTorch, and/or TensorFlow.
- Proven experience designing and training ML models across supervised, unsupervised, and self-supervised learning paradigms.
- Hands-on experience building and maintaining ML training and inference pipelines at scale.
- Solid MLOps experience: containerization, orchestration, CI/CD, model monitoring, and cloud deployment (AWS, GCP, or Azure).
- Demonstrated experience with at least two major Generative AI platforms (proprietary or open-source).
- Deep understanding of Transformer architecture and large language model internals.
- Practical experience implementing RAG systems with vector databases (Pinecone, Weaviate, ChromaDB, Qdrant, pgvector, Milvus etc.).
- Hands-on experience with Agentic AI frameworks and multi-step reasoning pipelines.
- Working knowledge of Low-Rank Adaptation (LoRA/QLoRA) and other PEFT fine-tuning methods.
- Strong understanding of evaluation frameworks for both discriminative and generative models.
Preferred Qualifications
- Experience with multimodal models (vision-language, text-to-image, speech-to-text).
- Familiarity with model quantization techniques (GPTQ, AWQ, bitsandbytes) for efficient inference.
- Exposure to graph neural networks, time-series forecasting, or reinforcement learning.
- Contributions to open-source ML or GenAI projects.
- Experience with advanced vector search strategies: ColBERT, hybrid BM25 + ANN, reranking models.
- Knowledge of responsible AI principles, bias mitigation, and model safety evaluation.
- Publications or patents in machine learning or AI domains.
Technical Skills Matrix
| Domain | Technologies & Tools |
| ML Frameworks | PyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, LightGBM |
| GenAI Platforms | OpenAI, Anthropic Claude, Google Gemini, Cohere, Amazon Bedrock |
| Open-Source LLMs | LLaMA 2/3, Mistral, Mixtral, Falcon, Phi-3, Gemma, Qwen |
| RAG & Vector DBs | LangChain, LlamaIndex, Pinecone, Weaviate, ChromaDB, pgvector, Qdrant |
| Agentic AI | LangGraph, AutoGen, CrewAI, Semantic Kernel, OpenAI Assistants API |
| Fine-Tuning (PEFT) | LoRA, QLoRA, Prefix Tuning, Prompt Tuning, Adapters, RLHF, DPO |
| MLOps | MLflow, Weights & Biases, DVC, Kubeflow, Airflow, BentoML, Seldon |
| Cloud & Infra | AWS SageMaker, GCP Vertex AI, Azure ML, Docker, Kubernetes |
| Experiment Tracking | MLflow, W&B, Comet ML, Neptune |
| Programming | Python, SQL, Bash, any frontend tech react/ angular; optional: Rust, Go for serving components |
Frequently Asked Questions
What is the salary for the [Software Services] : Sr. AI/ML Developer role at vinove?
The listed salary for this [Software Services] : Sr. AI/ML Developer position at vinove is INR 1800K–2200K. This is an FULL TIME role.
Where is the [Software Services] : Sr. AI/ML Developer position at vinove located?
This [Software Services] : Sr. AI/ML Developer role at vinove is based in Noida, UP, India. The position is listed as on-site or hybrid. Check the full job description or apply directly to confirm the work arrangement.
Is the [Software Services] : Sr. AI/ML Developer role at vinove full-time or part-time?
This is listed as a FULL TIME position. It is posted as a [Software Services] : Sr. AI/ML Developer role in the Software Delivery department at vinove.
Which team or department does the [Software Services] : Sr. AI/ML Developer at vinove belong to?
This [Software Services] : Sr. AI/ML Developer position is part of the Software Delivery department at vinove. See the full job description for more information about the team structure and responsibilities.
How do I apply for the [Software Services] : Sr. AI/ML Developer position at vinove?
Click the "Apply Now" button on this page. You will be redirected to vinove's official application portal hosted on keka where you can submit your application directly.
When was the [Software Services] : Sr. AI/ML Developer job at vinove posted?
This [Software Services] : Sr. AI/ML Developer position at vinove was posted on May 15, 2026. Apply as soon as possible — early applications are often reviewed first.
[Software Services] : Sr. AI/ML Developer
vinove · 💰 INR 1800K–2200K
You'll be redirected to vinove's official application page on keka.