Duties & Responsibilities
As an Analytics & AI Engineer, you will apply your expertise in machine learning, deep learning, and computer vision to build intelligent systems that drive business impact. You will focus on designing and deploying robust, scalable ML models that support real-time applications across edge and cloud environments. Familiarity with Large Language Models (LLMs) is a plus. We value continuous learning and expect you to stay current with the latest ML advancements to fuel innovation across the organization.

"1. Machine Learning & Deep Learning Model Development
Design, train, and optimize ML and deep learning models for applications such as image classification, object detection, and segmentation.
Research and implement state-of-the-art ML techniques across structured and unstructured data.
Monitor trends in ML/AI, adopting new methodologies to maintain technical edge."

"2. System Performance & Optimization
Implement parallel computing techniques (multi-threading, GPU acceleration) using Python or C++.
Optimize inference pipelines for minimal latency and resource efficiency in production environments.
Scale model serving infrastructure for robust performance across edge and cloud platforms."

"3. Cross-functional Collaboration & Innovation
Partner with product, engineering, and analytics teams to identify use cases and develop ML-driven solutions aligned with business needs.
Coordinate with data and MLOps teams to build reliable data pipelines and end-to-end ML workflows.
Lead rapid prototyping and experimentation initiatives to test new ideas and approaches."

"4. Data & Model Lifecycle Management
Create reusable frameworks for model training, evaluation, versioning, and deployment.
Ensure reproducibility, scalability, and maintainability through best practices (e.g., Git, Docker, CI/CD).
Continuously monitor model performance and implement updates to maintain accuracy and relevance."

"5. Business Impact & Communication
Translate complex ML concepts into clear, actionable insights for both technical and non-technical audiences.
Deliver ML solutions that support strategic objectives while delivering measurable results.
Advocate for data-driven decision-making and champion ML across the organization."

Job Requirements
Education & Experience
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related STEM field.
2–3 years of professional experience in ML roles, with hands-on work in computer vision or similar domains.
Demonstrated success in applying ML models to solve real-world problems.

Technical Expertise
Proficient in Python and/or C++ with experience in parallel and high-performance computing.
Strong experience with deep learning frameworks such as PyTorch or TensorFlow.
Skilled in optimizing model performance for memory, CPU, and GPU usage in production systems.
Experience with vision toolkits (e.g., OpenCV, NVIDIA libraries) and ML infrastructure tools (e.g., Docker, Kubernetes).

LLM Knowledge (Bonus)
Exposure to Large Language Models or NLP applications is an added advantage.

Other Skills
Effective communicator able to bridge gaps between technical and business teams.
Self-starter with strong analytical and problem-solving skills.
Passion for continuous learning and keeping pace with the evolving AI/ML ecosystem.