Machine Learning & Generative AI Engineer (Female Candidates)

Impronics Technologies; Bengaluru; Hybrid; INR 1,000,000 - 2,000,000; Full time

Job summary

Impronics Technologies is seeking a talented Machine Learning & Generative AI Engineer to design, build, and deploy innovative AI solutions. This role entails working with advanced AI and LLM technologies, including Transformer architectures and RAG systems.

Offering a hybrid work model, the successful candidate will collaborate with cross-functional teams, ensuring model safety and performance while driving impactful AI projects.

Qualifications

• 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 and prompt optimization.
• Proficiency in building RAG pipelines with vector databases.

Responsibilities

• Design, develop, and deploy end-to-end ML and Generative AI pipelines.
• Build and optimize LLM-based solutions leveraging Transformer models.
• Implement RAG systems using embeddings and vector databases.
• Fine-tune and customize LLMs on domain-specific datasets.
• Collaborate with cross-functional teams to align AI solutions with business goals.

Skills

Machine Learning

Deep Learning

Python

PyTorch

TensorFlow

Scikit-Learn

MLOps

Docker

Kubernetes

Generative AI

Tools

Azure

AWS

GCP

FAISS

Pinecone

Weaviate

Chroma

Airflow

Kubeflow

MLflow

Job description

Machine Learning & Generative AI Engineer (Female Candidates)

3+ Years

Full-Time

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

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.