Machine Learning & Generative AI Engineer (Female Candidates)

Summary: Designs, builds, and deploys ML and generative AI solutions, including LLM-based systems, RAG pipelines, and fine-tuned models for enterprise use cases.

Job Title: Machine Learning & Generative AI Engineer (Hybrid – 2–3 Days Work from Office)

Location: Bangalore

Note: Female Candidates Only

### Job Summary

We are seeking a talented Machine Learning & Generative AI Engineer to design, build, and deploy next-generation AI solutions. This role involves working on cutting-edge AI and LLM technologies, including Transformer architectures, RAG pipelines, fine-tuning, and prompt optimization, alongside developing traditional ML models for automation and decision intelligence.

This position offers a unique opportunity to innovate, experiment, and drive impactful AI initiatives within a leading enterprise environment.

### Key Responsibilities

Design, develop, and deploy end-to-end ML and Generative AI pipelines, including data preprocessing, model training, and scalable deployment.

Build and optimize LLM-based solutions leveraging Transformer models (BERT, GPT, T5, LLaMA, etc.).

Implement RAG (Retrieval-Augmented Generation) systems using embeddings and vector databases such as FAISS, Pinecone, Weaviate, or Chroma.

Fine-tune and customize LLMs on domain-specific datasets for enterprise use cases (Q&A, summarization, conversational AI, structured-to-text generation).

Work with MLOps tools (Airflow, Kubeflow, MLflow) to automate and manage ML lifecycle processes.

Deploy models efficiently using Docker, Kubernetes, and cloud platforms (Azure, AWS, or GCP).

Collaborate with cross-functional teams—data scientists, engineers, and domain experts—to align AI solutions with business goals.

Ensure model safety, privacy, performance, and adherence to responsible AI practices.

### Required Skills & Experience

3–7 years of experience in Machine Learning, Deep Learning, or AI model development.

Strong expertise in Python, PyTorch, TensorFlow, Scikit-Learn, and MLflow.

Hands-on experience with Transformer architectures and attention mechanisms.

Experience in Generative AI, including LLM fine-tuning (LoRA, QLoRA, PEFT, full model tuning), instruction tuning, and prompt optimization.

Proficiency in building RAG pipelines with vector databases (FAISS, Pinecone, Weaviate, Chroma).

Solid foundation in statistics, probability, and optimization techniques.

Experience with cloud ML platforms – Azure ML / Azure OpenAI, AWS SageMaker / Bedrock, or GCP Vertex AI.

Familiarity with Big Data tools (Spark, Hadoop, Databricks) and databases (SQL/NoSQL).

Knowledge of CI/CD pipelines, MLOps, and container orchestration (Docker, Kubernetes).

### Good to Have

Experience with NLP and Computer Vision (Transformers, BERT/GPT, YOLO, OpenCV).

Exposure to vector search, enterprise GenAI, and multimodal AI (text + image/video/audio).

Understanding of RLHF (Reinforcement Learning with Human Feedback).

Experience with Edge AI, federated learning, or real-time model serving.

### Core Competencies (Client Focus Areas)

Real-World Application & Deployment

Prompt Engineering & Optimization

Model Evaluation & Safety

Fine-Tuning & Customization

Strategic Thinking & Innovation

Understanding of LLM Internals

Tooling & Ecosystem Knowledge

Experimentation & Evaluation

Security, Privacy & Ethics

Problem Solving & Creativity

Business Alignment