AI / ML Engineer - 0–12+ Years Experience
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Key skills for this role
About the Role
Datamatics Technologies is seeking AI/ML Engineers across multiple experience levels (T1-T5) to design, develop, train, deploy, and optimize machine learning models and AI solutions.
Key Skills for This Role
Responsibilities
- Design, develop, train, evaluate, and deploy machine learning and AI solutions
- Build scalable ML pipelines from data preparation through production deployment
- Develop supervised, unsupervised, deep learning, and generative AI models
- Perform feature engineering, data preprocessing, model validation, and hyperparameter optimization
- Integrate ML models into enterprise applications and cloud native environments
- Deploy AI models using managed cloud ML services and MLOps practices
- Monitor model performance, drift, accuracy, and production reliability
- Collaborate with Data Scientists, Data Engineers, Software Engineers, and DevOps teams
- Optimize model performance, scalability, and inference latency
- Document models, experiments, evaluation metrics, deployment processes, and governance standards
- Follow AI security, responsible AI, and model governance best practices
Requirements
- Bachelor's or Master's degree in Computer Science, AI, ML, Data Science, Software Engineering, or related field
- Strong understanding of statistics, machine learning algorithms, deep learning, and generative AI concepts
- Experience with cloud AI platforms and modern ML frameworks
- Knowledge of MLOps, CI/CD, model deployment, and production monitoring is an advantage
- Strong analytical, communication, and problem solving skills
- Ability to work in Agile, cross functional, and enterprise scale environments
Full Job Posting
Job Summary
- We are seeking AI / ML Engineers across multiple experience levels (T1–T5) to design, develop, train, deploy, and optimize machine learning models and AI solutions throughout the complete machine learning lifecycle.
- Candidates will work on data preparation, feature engineering, model development, evaluation, deployment, monitoring, and continuous improvement using modern cloud AI platforms and open source machine learning frameworks.
- The role offers opportunities ranging from entry level implementation to enterprise AI architecture and technical leadership.
Key Responsibilities
- Design, develop, train, evaluate, and deploy machine learning and AI solutions
- Build scalable ML pipelines from data preparation through production deployment
- Develop supervised, unsupervised, deep learning, and generative AI models
- Perform feature engineering, data preprocessing, model validation, and hyperparameter optimization
- Integrate ML models into enterprise applications and cloud native environments
- Deploy AI models using managed cloud ML services and MLOps practices
- Monitor model performance, drift, accuracy, and production reliability
- Collaborate with Data Scientists, Data Engineers, Software Engineers, and DevOps teams
- Optimize model performance, scalability, and inference latency
- Document models, experiments, evaluation metrics, deployment processes, and governance standards
- Follow AI security, responsible AI, and model governance best practices
Required Technical Skills
- Cloud AI Platforms: GCP Vertex AI or BigQuery ML or Dataflow; Azure ML or Azure OpenAI; AWS SageMaker or Amazon Bedrock
- Programming: Python
- Machine Learning Frameworks: TensorFlow or PyTorch
- Generative AI & LLM Frameworks: HuggingFace or LangChain
- Data & Analytics: Databricks
- Additional Skills: Machine Learning, Deep Learning, NLP, Computer Vision, Model Evaluation, Feature Engineering, API Development, Git
Preferred Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related field
- Strong understanding of statistics, machine learning algorithms, deep learning, and generative AI concepts
- Experience with cloud AI platforms and modern ML frameworks
- Knowledge of MLOps, CI/CD, model deployment, and production monitoring is an advantage
- Strong analytical, communication, and problem solving skills
- Ability to work in Agile, cross functional, and enterprise scale environments
Responsibilities by Tier
- T1 – Associate AI / ML Engineer (0–2 Years): Assist in data preparation, develop simple models, support training/testing, deploy under guidance, maintain documentation, debug pipelines, learn cloud platforms.
- T2 – AI / ML Engineer (2–4 Years): Build and deploy production ready models, perform feature engineering and optimization, develop reusable components and APIs, implement monitoring, integrate into applications.
- T3 – Senior AI / ML Engineer (5–7 Years): Design end to end solutions, lead complex pipelines, optimize training, guide juniors, implement Responsible AI, improve reliability, collaborate with stakeholders.
- T4 – Lead AI / ML Engineer (8–11 Years): Lead architecture and delivery of enterprise AI platforms, define standards, drive cloud native design, mentor teams, collaborate with enterprise architects.
- T5 – Principal AI / ML Architect (12+ Years): Define enterprise AI strategy, own architecture decisions, lead transformation, establish governance, evaluate emerging tech, provide executive guidance.
Preferred Certifications
- Google Professional Machine Learning Engineer
- AWS Certified Machine Learning – Specialty
- Microsoft Certified: Azure AI Engineer Associate
- TensorFlow Developer Certificate
Expected Deliverables
- Machine Learning Model Documentation
- Model Training & Evaluation Reports
- Feature Engineering Documentation
- Model Cards
- Production Deployment Pipelines
- Monitoring & Performance Dashboards
- AI Solution Design Documents
- Model Validation Reports
- Inference APIs
- Production Ready ML Models
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