Artificial Intelligence Engineer Python

Job description

<p><p><b>Required skills/Competencies : </b><br/><br/><b>Programming Languages : </b></p><p><p><b><br/></b></p><br/>- Strong in Python, data structures, and algorithms.<br/><br/></p><p><br/>- Hands-on with NumPy, Pandas, Scikit-learn for ML prototyping.<br/><br/><b>Machine Learning Frameworks : </b><br/><br/></p><p><br/>- Understanding of supervised/unsupervised learning, regularization, feature engineering, model selection, cross-validation, ensemble methods (XGBoost, LightGBM).<br/><br/><b>Deep Learning Techniques : </b><br/><br/></p><p><br/>- Proficiency with PyTorch or TensorFlow/Keras<br/><br/></p><p><br/>- Knowledge of CNNs, RNNs, LSTMs, Transformers, Attention mechanisms.<br/><br/></p><p><br/>- Familiarity with optimization (Adam, SGD), dropout, batch norm.<br/><br/><b>LLMs & RAG : </b><br/><br/></p><p><br/>- Hugging Face Transformers (tokenizers, embeddings, model fine-tuning).<br/><br/></p><p><br/>- Vector databases (Milvus, FAISS, Pinecone, ElasticSearch).<br/><br/></p><p><br/>- Prompt engineering, function/tool calling, JSON schema outputs.<br/><br/><b>Data & Tools : </b><br/><br/></p><p><br/>- SQL fundamentals; exposure to data wrangling and pipelines.<br/><br/></p><p><br/>- Git/GitHub, Jupyter, basic Docker.<br/><br/><b>What are we looking for?

: </b><br/><br/></p><p><br/>- Solid academic foundation with strong applied ML/DL exposure.<br/><br/></p><p><br/>- Curiosity to learn cutting-edge AI and willingness to experiment.<br/><br/></p><p><br/>- Clear communicator who can explain ML/LLM trade-offs simply.<br/><br/></p><p><br/>- Strong problem-solving and ownership mindset.<br/><br/><b>Minimum Qualifications : </b><br/><br/></p><p><br/>- 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.<br/><br/>- Developing and delivering parts of a product, in accordance with the customers requirements and organizational quality norms.

Activities to be performed include : <br/><br/></p><p><br/>- Requirement analysis and design of software solutions based on requirements and architectural /design guidelines.<br/><br/></p><p><br/>- Implementation of features and/or bug-fixing and delivering solutions in accordance with coding guidelines and on-time with high quality.<br/><br/></p><p><br/>- Identification and implementation of unit and integration tests to ensure solution addresses customer requirements, and quality, security requirements of product are met.<br/><br/></p><p><br/>- Performing code review and creation / support for relevant documentation (requirement/design/test specification).<br/><br/></p><p><br/>- Ensuring integration and submission of solution into software configuration management system, within committed delivery timelines.<br/><br/></p><p><br/>- Performing regular technical coordination / review with stake holders and ensuring timely reporting and escalations if any.<br/><br/><b>Job Requirements/Skills : </b><br/><br/></p><p><br/>- Translate ambiguous business problems into analytical/ML approaches; define success metrics and experiment design (A/B, DoE).<br/><br/></p><p><br/>- Explore & prepare dataprofiling, feature engineering, handling bias/leakage, data quality checks.<br/><br/></p><p><br/>- Build and validate models (supervised/unsupervised/time series/NLP/CV as relevant); compare baselines and SOTA methods.<br/><br/></p><p><br/>- Ship insights and/or modelsdashboards, notebooks, or production endpoints with proper monitoring.<br/><br/></p><p><br/>- Communicate results clearly to technical and non-technical stakeholders, document assumptions and limitations.<br/><br/></p><p><br/>- Contribute to data governance and reusable tooling (feature stores, evaluation frameworks).<br/><br/><b>Required qualifications : </b><br/><br/></p><p><br/>- 4 to 7 years in data Proficiency in Python (pandas, NumPy, scikit-learn); SQL fluency for large datasets.<br/><br/></p><p><br/>- Solid statistics/experimental design (hypothesis testing, causal inference basics, confidence intervals).<br/><br/></p><p><br/>- Experience building and validating models end-to-end; familiarity with model evaluation and error analysis.<br/><br/></p><p><br/>- Strong storytellingability to translate findings into business recommendations.<br/><br/><b>Nice to have : </b><br/><br/></p><p><br/>- Experience with one ofNLP (spaCy, Hugging Face), CV (OpenCV, TorchVision), Recommenders, Time Series (Prophet, statsmodels).<br/><br/></p><p><br/>- Familiarity with ML in production (FastAPI/Flask, model registries, feature stores).<br/><br/></p><p><br/>- Cloud & data stackAWS/GCP/Azure, Spark, dbt, Airflow; BI (Power Version control & workflowsgit, CI/CD, experiment tracking (MLflow/Weights & Biases).</p><br/></p> (ref:hirist.tech)

Required Skill Profession

Computer Occupations