AI/ML Engineer III

Job Description

Translate business goals into measurable ML goals (KPIs, acceptance thresholds) in collaboration with PMs and data scientists.

Lead the translation of ambiguous product needs into clear ML metrics and success criteria.

Own the full lifecycle from prototyping (incl. deep learning and GenAI) to deployment and monitoring.

Develop and maintain observability dashboards and alerts tied to ML metrics and feature drift.

Run and safeguard models in real time

Champion cross-functional collaboration & governance

Pilot new ML tools/frameworks, leading integration into production where appropriate.

Architect data strategy, championing reproducibility, traceability, and quality across the ML stack

Spearhead adoption of emerging ML trends; run strategic POCs and lead production rollouts of state-of-the-art techniques.

Act as a cross-org ML thought leader—aligning product, infra, legal, and UX on responsible ML.

Key Deliverables by Level

• Level 3: AI/ML Engineer III
• Scalable ML pipelines with automated training, validation, and deployment workflows
• Deployed ML solutions integrated with Astreya’s managed service platforms (e.g., NLP for ticket routing)
• Dashboards for monitoring inference quality and data drift
• MLOps pipelines with CI/CD practices

Essential Duties and Responsibilities (All Levels):

• Assist in data cleaning, feature engineering, testing basic ML models, write and debug simple scripts
• Develop ML modules, assist in deployment, support data pipelines, contribute to documentation and unit testing
• Support data preparation, model training under guidance, debug code, attend knowledge sessions
• Develop and maintain smaller AI modules (e.g., anomaly detection), assist in deployments, write technical documentation
• Lead development of scalable ML models, integrate into ITSM systems, ensure compliance and performance metrics
• Architect end-to-end AI platforms, oversee cross-domain projects (e.g., NLP for service desk, CV for asset tracking)
• Lead ML solution design, own production deployments, optimize inference models, drive MLOps practices
• Architect end-to-end solutions for AI-driven services (e.g., IT ticket routing, network anomaly detection), lead AI projects

Education and/or Work Experience Requirements:

Minimum Requirements:

• Bachelor’s degree in Computer Science, Data Science, IT, or a related field.
• Master’s preferred or equivalent experience for senior levels
• Level 3: 4–6 years experience in ML/AI implementation and deployment

Preferred Certifications (All Levels):

• Google Cloud Professional Machine Learning Engineer
• TensorFlow Developer Certificate

Knowledge, Skills & Abilities (KSAs):

• Machine Learning techniques (regression, classification, clustering)
• Deep Learning architectures (CNNs, RNNs, Transformers, LLMs)
• NLP (tokenization, BERT, prompt engineering)
• Big Data fundamentals (Spark, Hadoop)
• Model interpretability, ethics in AI, bias detection
• Cloud-native AI services (GCP Vertex AI)
• Data governance, security, and ethical AI practices
• Programming: Python, Apps Script, SQL
• Frameworks: TensorFlow, PyTorch, scikit-learn, HuggingFace
• Tools: Git, Docker, Kubernetes, Airflow, MLflow, Jupyter, Postman
• Data pipeline skills: SQL, Pandas, data APIs
• Deployment: Flask/FastAPI, CI/CD, REST APIs, cloud functions
• Strong analytical and debugging skills
• Translate business problems into AI solutions
• Communicate effectively with technical and non-technical stakeholders
• Work under Agile or DevOps-based workflows
• Stay current with research and emerging technologies
• Rapidly learn new AI concepts and tools
• Translate business challenges into ML solutions
• Communicate technical findings to non-technical stakeholders
• Handle ambiguity and balance research with delivery
• Collaborate across globally distributed teams

Competency

• Technical Expertise
• Understands basic ML/DL principles
• Codes in Python/R
• Familiarity with AI/ML tools such as Jupyter, scikit-learn, or TensorFlow (basic use)
• Applies supervised/unsupervised ML methods
• Proficient in TensorFlow/PyTorch
• Uses cloud ML services
• Familiar with ML pipelines
• Documents technical solutions and contributes to code reviews
• Designs and builds production-grade models
• Uses MLflow, Airflow, CI/CD tools
• Experience with model deployment and monitoring
• Owns end-to-end AI/ML solutions including architecture, training, deployment, and monitoring
• Leads development of enterprise-wide AI/ML strategies and platforms
• Drives model optimization at scale
• Understands data engineering best practices
• Defines org-wide AI/ML standards
• Oversees architecture for reusable platforms
• Directs ML model governance and compliance
• Evaluates and mitigates risks related to fairness, privacy, and regulatory requirements

Problem Solving & Innovation

• Solves small coding and data cleaning problems
• Ability to analyze and clean datasets
• Identifies root causes in data/model issues
• Applies ML solutions to scoped problems
• Effective in debugging and troubleshooting code and data issues
• Selects and tunes algorithms for real-world impact
• Innovates within team on novel use cases
• Anticipates platform-wide AI needs
• Designs scalable solutions to business-wide problems
• Champions reusability and standardization across teams
• Designs AI architectures integrated into critical systems (e.g., service desks, observability)
• Drives disruptive AI innovation
• Aligns AI/ML initiatives with enterprise transformation goals
• Provides strategic oversight for all AI initiatives and cross-org alignment

Collaboration & Communication

• Good communication and team collaboration skills
• Shares ideas in meetings
• Communicates findings clearly to peers and stakeholders