Artificial Intelligence Engineer

Required skills/Competencies :

Programming Languages :

- Hands-on with NumPy, Pandas, Scikit-learn for ML prototyping.

Machine Learning Frameworks :

- Understanding of supervised/unsupervised learning, regularization, feature engineering, model selection, cross-validation, ensemble methods (XGBoost, LightGBM).

Deep Learning Techniques :

- Proficiency with PyTorch or TensorFlow/Keras

- Knowledge of CNNs, RNNs, LSTMs, Transformers, Attention mechanisms.

- Familiarity with optimization (Adam, SGD), dropout, batch norm.

LLMs & RAG :

- Hugging Face Transformers (tokenizers, embeddings, model fine-tuning).

- Vector databases (Milvus, FAISS, Pinecone, ElasticSearch).

- Prompt engineering, function/tool calling, JSON schema outputs.

Data & Tools :

- SQL fundamentals; exposure to data wrangling and pipelines.

- Git/GitHub, Jupyter, basic Docker.

What are we looking for? :

- Solid academic foundation with strong applied ML/DL exposure.

- Curiosity to learn cutting-edge AI and willingness to experiment.

- Clear communicator who can explain ML/LLM trade-offs simply.

- Strong problem-solving and ownership mindset.

Minimum Qualifications :

- Doctorate (Academic) Degree and 2 years related work experience; Master's Level Degree and related work experience of 5 years; Bachelor's Level Degree and related work experience of 7 years in building AI systems/solutions with Machine Learning, Deep Learning, and LLMs.

- Developing and delivering parts of a product, in accordance with the customers requirements and organizational quality norms. Activities to be performed include :

- Requirement analysis and design of software solutions based on requirements and architectural /design guidelines.

- Implementation of features and/or bug-fixing and delivering solutions in accordance with coding guidelines and on-time with high quality.

- Identification and implementation of unit and integration tests to ensure solution addresses customer requirements, and quality, security requirements of product are met.

- Performing code review and creation / support for relevant documentation (requirement/design/test specification).

- Ensuring integration and submission of solution into software configuration management system, within committed delivery timelines.

- Performing regular technical coordination / review with stake holders and ensuring timely reporting and escalations if any.

Job Requirements/Skills :

- Translate ambiguous business problems into analytical/ML approaches; define success metrics and experiment design (A/B, DoE).

- Explore & prepare dataprofiling, feature engineering, handling bias/leakage, data quality checks.

- Build and validate models (supervised/unsupervised/time series/NLP/CV as relevant); compare baselines and SOTA methods.

- Ship insights and/or modelsdashboards, notebooks, or production endpoints with proper monitoring.

- Communicate results clearly to technical and non-technical stakeholders, document assumptions and limitations.

- Contribute to data governance and reusable tooling (feature stores, evaluation frameworks).

Required qualifications :

to 7 years in data science/analytics/ML.

- Proficiency in Python (pandas, NumPy, scikit-learn); SQL fluency for large datasets.

- Solid statistics/experimental design (hypothesis testing, causal inference basics, confidence intervals).

- Experience building and validating models end-to-end; familiarity with model evaluation and error analysis.

- Strong storytellingability to translate findings into business recommendations.

Nice to have :

- Experience with one ofNLP (spaCy, Hugging Face), CV (OpenCV, TorchVision), Recommenders, Time Series (Prophet, statsmodels).

- Familiarity with ML in production (FastAPI/Flask, model registries, feature stores).

- Cloud & data stackAWS/GCP/Azure, Spark, dbt, Airflow; BI (Power BI/Tableau/Looker).

- Version control & workflowsgit, CI/CD, experiment tracking (MLflow/Weights & Biases).