Ai Ml Engineer

1. Technical Skills:
• Strong Work Experience of 3-4 years in AI domain
• Strong programming skills in Python (preferred), with proficiency in libraries like TensorFlow, PyTorch, or Scikit-learn.
• Knowledge of ML operations (MLOps) and experience in deploying models in production environments.
• Solid understanding of traditional ML models (e.g., regression, SVM, decision trees, random forests).
• Exposure to large language models (e.g., OpenAI GPT, Llama, BERT) and fine-tuning techniques.
2. Data Expertise:
• Hands-on experience with data cleanup, preprocessing, and feature engineering.
• Proficiency with data handling tools such as Pandas, NumPy, and SQL.
3. Architecture and Systems:
• Understanding of software architecture and experience contributing to system design.
• Familiarity with cloud platforms (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes) would be a big plus .
4. Soft Skills:
• Strong analytical and problem-solving abilities.
• Excellent communication skills with the ability to present ideas effectively.
• Proven ability to work collaboratively in a team-oriented environment.
5. Education:
• B.Tech/M.Tech/MCA in first Class (CS/IT/ECE) From Tier 1 College
6. AI/ML Development:
• Design, implement, and optimize AI pipelines, including data ingestion, preprocessing, model training, and deployment.
• Develop and fine-tune traditional machine learning models and integrate them into production systems.
• Work on cutting-edge projects involving LLMs, including prompt engineering, fine-tuning, and inference optimization.
7. Data Engineering:
• Perform data cleanup and preprocessing, ensuring data quality and consistency.
• Develop and implement feature engineering pipelines to enhance model performance.
• Collaborate with data engineers to manage large-scale datasets efficiently.
8. Software Development:
• Write clean, maintainable, and efficient code in programming languages such as Python, or Go.
• Build scalable, robust, and maintainable solutions for AI/ML models and their deployment.
• Contribute to architecture design and participate in code reviews to ensure best practices.
9. Collaboration and Communication:
• Clearly communicate technical concepts and progress to both technical and non-technical stakeholders.
• Act as a team player, contributing to a collaborative and innovative work environment.
10. Continuous Learning:
• Stay updated on the latest developments in AI/ML, including advancements in LLMs and model optimization techniques.
• Explore and evaluate emerging AI tools and technologies for potential integration into workflows.Role & responsibilities

Preferred candidate profile

Perks and benefits