Machine Learning Engineering Senior Engineer #
Job Description:
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.  
 
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

 
Skills Required: Python, CI-CD, LLM, Deeplearning, API, AI/ML, ALGORITHMS
 
Skills Preferred: Big Query
 
Experience Required: Senior Engineer Exp: Prac. In 2 coding lang. or adv. Prac. in 1 lang.; guides. 8+ Years of Exp.
 
Experience Preferred:

• CI/CD for ML (GitHub Actions, Jenkins) 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.
• 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

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

• 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.