Machine Learning Engineer

About the Company

Employees in this job function are responsible for designing, building, deploying and scaling complex self-running ML solutions in areas like computer vision, perception, localization etc. They also automate and optimize the end-to-end ML model lifecycle using their expertise in experimental methodologies, statistics, and coding for tool building and analysis.

About the Role

A short paragraph summarizing the key role responsibilities.

Responsibilities

• Collaborate with business and technology stakeholders to understand current and future ML requirements
• Design and develop innovative ML models and software algorithms to solve complex business problems in both structured and unstructured environments
• Design, build, maintain and optimize scalable ML pipelines, architecture and infrastructure
• Use machine language and statistical modeling techniques such as decision trees, logistic regression, Bayesian analysis and others to develop and evaluate algorithms to improve product/system performance, quality, data management and accuracy
• Adapt machine learning to areas such as virtual reality, augmented reality, object detection, tracking, classification, terrain mapping, and others.
• Train and re-train ML models and systems as required
• Deploy ML models and algorithms into production and run simulations for algorithm development and test various scenarios
• Automate model deployment, training and re-training, leveraging principles of agile methodology, CI/CD/CT (Continuous Integration/ Continuous Deployment/ Continuous Training) and MLOps
• Enable model management for model versioning and traceability to ensure modularity and symmetry across environments and models for ML systems

Qualifications

• Education Required: Bachelor's Degree, Master's Degree
• Education Preferred: Certification Program

Required Skills

• Python
• CI-CD
• LLM
• Deeplearning
• API
• AI/ML
• ALGORITHMS

Preferred Skills

• Big Query

Pay range and compensation package

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Equal Opportunity Statement

Employees in this job function are responsible for predicting and/or extracting meaningful trends/patterns/recommendations from raw data, leveraging data science methodologies including Machine Learning (ML), predictive modeling, math, statistics, advanced analytics, etc.

Key Responsibilities

• Understand business requirements and analyze datasets to determine suitable approaches to meet analytic business needs and support data-driven decision-making
• Design and implement data analysis and ML models, hypotheses, algorithms and experiments to support data driven decision-making
• Apply various analytics techniques like data mining, predictive modeling, prescriptive modeling, math, statistics, advanced analytics, machine learning models and algorithms, etc.; to analyze data and uncover meaningful patterns, relationships, and trends
• Design efficient data loading, data augmentation and data analysis techniques to enhance the accuracy and robustness of data science and machine learning models, including scalable models suitable for automation
• Research, study and stay updated in the domain of data science, machine learning, analytics tools and techniques etc.; and continuously identify avenues for enhancing analysis efficiency, accuracy and robustness

Additional Safety Training/Licensing/Personal Protection Requirements

Ability to design end-to-end ML system architecture with:

• Model orchestration (LLM + OCR + embeddings + prompt pipelines)
• Preprocessing for images/PDF/PPT/Excel
• Embedding store, vector DB, or structured extraction systems
• Async processing queue, job orchestration, microservice design
• GPU/CPU deployment strategy

Must be strong in scaling ML systems:

• Batch processing large files
• Handling concurrency, throughput, latency
• Model selection, distillation, quantization (GGUF, ONNX)
• CI/CD for ML (GitHub Actions, Jenkins)
• Model monitoring (concept drift, latency, cost optimization)
• Experience with cloud platforms: AWS/GCP/Azure with AI services (SageMaker, Vertex AI, Bedrocknice to have)

Problem-Solving & Solution Ownership:

• Able to identify the right ML approach (fine-tuning, retrieval, prompting, multimodal pipeline).
• Ability to break vague product problems into clear ML tasks.
• Skilled in PoC building, quick prototyping, and converting them into production systems.
• Capability to estimate feasibility, complexity, cost, and timelines of ML solutions.