Machine Learning Engineer

Experience

• 6+ years of experience in

  Python

  development
• 6+ years of hands-on experience in

  Machine Learning

  , specializing in

  Conversational AI (Audio)

  and

  Computer Vision

• 2+ years of experience in

  Generative AI / Large Language Models (LLMs)

Key Responsibilities

• Research, evaluate, and customize open-source

  Conversational AI

  ,

  Computer Vision and Generative AI models

  to address diverse real-world use cases.
• Optimize and deploy ML models on

  cloud platforms

  such as

  AWS

  ,

  GCP

  , or

  Azure

  with a focus on scalability and performance.
• Design and implement

  automation scripts

  for data processing, validation, and workflow orchestration across audio, image, video, and text modalities.
• Explore, preprocess, and analyze

  unstructured multimodal data

  (audio, image, video, text) to derive actionable insights.
• Develop and maintain

  MLOps pipelines

  for continuous integration, deployment, and monitoring of ML models.
• Build and manage

  shell (Bash)

  scripts for data handling, automation, and cloud resource management.
• Collaborate with cross-functional teams to translate research outcomes into production-ready AI systems.
  

Qualifications & Experience

• Bachelors or Masters degree in

  Computer Science

  ,

  Machine Learning

  ,

  Artificial Intelligence

  , or a related field.
• Proven expertise in

  Python

  with a strong foundation in

  object-oriented programming

  and

  design patterns

  .
• Deep hands-on experience in customizing and optimizing

  open-source ML models

  for Audio, Image, Video and Text.
• Solid understanding of

  MLOps

  principles, practices, and tools for versioning, automation, and observability.
• Proficiency with ML/DL frameworks such as

  Scikit, Sklearn, TensorFlow

  ,

  PyTorch

  , and

  Keras

  .
• Experience with Python frameworks such as

  FastAPI

  ,

  Django

  ,

  Celery

  , and

  Streamlit

  .
• Expertise in Speech Processing tasks, including Voice Activity Detection,

  Speaker Diarization, Speaker Embeddings

  , and

  Automatic Speech Recognition (ASR)

  .
• Strong background in

  Computer Vision

  tasks such as object detection, face recognition, and image classification.
• Familiarity with

  cloud-based SaaS environments

  and testing strategies to ensure model functionality, performance, and scalability.
• Experience with

  LangChain

  for building LLM-driven applications and

  LangGraph

  for multi-agent stateful workflows.
• Hands-on experience with

  vector databases

  (Pinecone, Weaviate, ChromaDB, Qdrant) for

  semantic search

  and

  RAG (Retrieval-Augmented Generation)

  .
• Proficiency in

  prompt engineering

  and optimizing

  LLM interactions

  for accuracy and contextual relevance.
• Excellent communication and collaboration skills, with the ability to explain complex technical concepts to both technical and non-technical stakeholders.
• Proven ability to work independently and lead initiatives in a dynamic, cross-functional environment.