[Software Services] : Sr. AI/ML Developer

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