AI/ML Engineer II

• 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., * 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

Level 3-5 Additional Responsibilities

• 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
• Define enterprise AI roadmap, establish AI governance, lead high-impact client engagements, scale AI maturity company-wide
• 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 metricsArchitect end-to-end AI platforms, oversee cross-domain projects (e.g., NLP for service desk, CV for asset tracking), Applies domain knowledge to improve model relevance (e.g., IT ops, cybersecurity)

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

Contributes to documentation and demos

Collaborates cross-functionally to integrate models into services

Explains model behavior to technical and semi-technical audiences

Coaches junior team members

Interprets results and presents actionable insights to stakeholders

Builds trust with cross-functional teams and leadership

Acts as primary AI contact for programs

Engages with external partners/vendors on AI innovation

Represents AI/ML direction in C-level and client-facing conversations

Evangelizes a culture of innovation and continuous learning in AI/ML

Project Ownership & Execution

Supports project tasks under supervision

Tracks simple work using task tools

Documents code and data usage

Delivers discrete ML components

Manages tasks independently

Leads projects through design, development, testing, and rollout

Owns project timeline and quality

Familiar with advanced ML topics (e.g., transformers, reinforcement learning, LLM fine-tuning)

Coordinates complex programs and integrations

Leads cross-functional AI initiatives

Drives data quality and governance initiatives for reliable model outcomes

Facilitates cross-functional solutioning between product, IT, and operations

Oversees multi-team programs

Owns delivery of strategic AI initiatives across departments

Defines AI success metrics, compliance frameworks, and model governance structures

Strategic Thinking & Leadership

Understands team mission

Adopts best practices

Takes direction and accepts feedback constructively

Builds and evaluates supervised/unsupervised models independently

Provides input on technical direction

Mentors junior engineers

Designs scalable models and pipelines for production use

Defines best practices and technical vision

Influences product and engineering roadmap

Balances model performance with business objectives and ethical guidelines

Sets the AI/ML vision and roadmap aligned with business growth goals

Establishes AI strategy, ethics, and governance

Influences external clients and industry engagement

• Level 1: 1-2 years in data science/ML roles; hands-on with frameworks like scikit-learn or PyTorch
• Level 2: 1-2 years in data science/ML roles; hands-on with frameworks like scikit-learn or PyTorch
• Level 3: 4-6 years experience in ML/AI implementation and deployment
• Level 4: 7-9 years experience; domain expertise (e.g., IT operations or security AI)
• Level 5: 10+ years in software engineering with AI/ML leadership experience

Preferred Certifications (All Levels)

• Google Cloud Professional Machine Learning Engineer
• AWS Certified Machine Learning - Specialty
• Microsoft Certified: Azure AI Engineer Associate
• TensorFlow Developer Certificate
• Databricks Certified Machine Learning Professional
• Kubernetes or Docker certification for MLOps roles

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 (AWS Sagemaker, GCP Vertex AI, Azure ML)
• Data governance, security, and ethical AI practices
• Programming: Python (must), Java/C++ (optional), 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

Competencies

Each level, 1 - 5, represents a progression in complexity, autonomy, and responsibility. The higher the level, the more critical thinking, leadership, and expertise are required., 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

We enable businesses to make better decisions, achieve operational efficiency and gain a competitive edge. The Astreya advantage is centered around focus and clear- vision, world-class talent, and innovative technology: Creativity is in our DNA., At Astreya, you have a chance to create change and build a new, better ‘normal’ based on how people really function and what allows us to thrive. We encourage our employees to explore the positive changes they can make within our personal and professional identities.

Work alongside data-driven technologists

As part of the Astreya team, you’ll have the opportunity to work with some of the largest and most innovative companies on the globe. You’ll gain exposure to empowering technologies as well as the newest and most efficient ways to work.