Department: Artificial Intelligence / Research & Engineering
About the Role
We’re looking for an Experienced Machine Learning Engineer with 4–6 years of hands-on experience in building real-world AI systems that deliver measurable impact. The ideal candidate is passionate about solving complex data problems, has a deep theoretical understanding of AI concepts, and thrives in a collaborative, fast-paced environment.
You will work across the end-to-end AI lifecycle — from data curation and model training to deployment, optimization, and monitoring. You’ll design high-performance inference pipelines and contribute to the development of scalable, reliable AI products.
Key Responsibilities
- Curate, clean, and align datasets across multiple modalities — Audio, Text, Tabular, and Time Series.
- Perform Model Selection, Training, Evaluation, and fine-tuning for production-grade systems.
- Develop and maintain Model Monitoring pipelines to ensure stability and performance.
- Design and implement Fast Inference Pipelines optimized for real-world deployment.
- Collaborate closely with Data Scientists, ML Engineers, and Product Teams to ship impactful AI features.
- Follow SDLC best practices — version control, testing, CI/CD, and code documentation.
- Research and experiment with new AI architectures, frameworks, and optimization techniques.
- (Bonus) Work with Agent Orchestration Frameworks such as LangChain, LangGraph, or CrewAI to build intelligent multi-agent systems.
Requirements
- 4–6 years of professional experience in Machine Learning or Applied AI roles.
- Strong understanding of AI/ML fundamentals — statistics, probability, optimization, and deep learning architectures.
- Proven experience working with real-world AI data pipelines and production environments.
- Proficiency in Python, with clean, modular, and well-documented coding style.
- Hands-on experience with ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
- Familiarity with data handling tools (Pandas, NumPy, SQL) and deployment tools (Docker, FastAPI, Kubernetes).
- Experience in MLOps concepts — monitoring, retraining, and automation.
- Excellent analytical, problem-solving, and communication skills.
- Bonus: Knowledge of LLM agent orchestration frameworks (LangChain, LangGraph, CrewAI) or vector databases (Pinecone, FAISS, Chroma).
Educational Requirements: B.Sc. in Computer Science & Engineering (CSE) or similar.
Workplace: Onsite
Employment Type: Full-Time
Salary: Negotiable (based on the Experiences and Expertise)
