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, Bedrock—nice 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.