AI / ML Engineer
Introduce AI capabilities only after Pulse has sufficient quality data and a measurable business case. Build explainable forecasting, anomaly detection and recommendation services using Python/FastAPI without weakening the reliability or simplicity of the core platform.
The facts before you apply
What you'll do and what you'll bring
Key Responsibilities
- Frame business problems, baselines, value metrics and data requirements
- Explore, clean and engineer features from governed operational data
- Build and evaluate forecasting, anomaly or recommendation models
- Expose approved models through versioned FastAPI services
- Implement experiment tracking, model registry, monitoring and rollback
- Assess drift, bias, explainability and human-review requirements
- Partner with product and domain experts on controlled pilots
- Document limitations, data lineage and responsible-use controls
Essential Qualifications & Skills
- Python, pandas, scikit-learn and statistical modelling
- Time-series/forecasting or anomaly-detection experience
- FastAPI and production API deployment
- Model evaluation, experiment tracking and monitoring
- SQL and data-quality engineering
- Ability to reject AI use cases that lack value or reliable data
Expected outcomes
- Select one high-value use case with baseline and success threshold
- Create reproducible training/evaluation pipeline
- Run offline validation and controlled pilot
- Deploy monitored service with fallback behaviour
- Publish model card and go/no-go evidence
Success measures
- Business uplift against baseline
- Model accuracy and drift
- Service reliability/latency
- Human override and harmful-error rate
Preferred qualifications and behavioural fit
Preferred Qualifications
Behavioural Competencies
What to expect if you join
The role works in a cross-functional, documentation-first and architecture-led product environment. Candidates must be comfortable with hands-on ownership, transparent decisions, code/design review, automated quality controls and production accountability. The six-month MVP excludes unnecessary microservices, Kubernetes, speculative AI and other scope not approved through product governance.
Application evidence: CV, role-relevant portfolio or work sample where applicable, and concise examples showing personal contribution and measurable outcomes. Final compensation, work arrangement and joining date will be confirmed during the recruitment process. DataArtha Solutions is committed to fair, respectful and merit-based selection.
Apply for AI / ML Engineer
If this role matches your experience, send your resume and a short note to careers@dataartha.in. We review every application and reach out if there's a good match for your profile.