Key Objectives:
- Translate business problems into ML/AI solutions with measurable impact.
- Build and optimize production-grade ML models that are scalable and reliable.
- Ensure robustness, fairness, and efficiency in ML pipelines.
- Drive adoption of AI/ML best practices across the organization.
Core Responsibilities
- Design and implement ML algorithms for prediction, classification, recommendation, NLP, or computer vision use cases.
- Collect, clean, and preprocess large datasets for model development.
- Develop, train, validate, and fine-tune machine learning / deep learning models.
- Deploy ML models into production using MLOps best practices (CI/CD pipelines, monitoring, retraining).
- Collaborate with cross-functional teams (data engineers, product managers, software developers) to integrate AI features into products.
- Continuously evaluate new ML techniques, frameworks, and research to improve model accuracy and performance.
- Document solutions, conduct knowledge-sharing sessions, and mentor junior engineers.
Must-Have Skills (Technical & Soft)Technical:
- Strong programming skills inPython(NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).
- Experience withmachine learning algorithms(supervised, unsupervised, reinforcement learning).
- Hands-on expertise indeep learning(CNNs, RNNs, Transformers).
- Proficiency indata preprocessing, feature engineering, and statistical analysis.
- Experience withSQL/NoSQL databasesand handling large datasets.
- Familiarity withMLOps tools(MLflow, Kubeflow, Airflow, Docker, Kubernetes).
- Knowledge of cloud platforms (AWS Sagemaker, Azure ML, GCP AI Platform).
- Generative AI models(e.g., LLMs, GANs, Diffusion Models, VAEs, Transformers).
- Fine-tune and optimizefoundation modelsfor domain-specific applications
- Agentic AI framework(AutoGen, LangChain/LangGraph, CrewAI, and Microsoft Semantic Kernel, OpenAI)
- Experience inmultimodal AI(text, image, audio, video generation).
- Familiarity with prompt engineering & fine-tuning LLMs.
- Knowledge of vector databases (Pinecone, Weaviate, FAISS, Milvus etc) for retrieval-augmented generation (RAG)
Soft Skills:
- Strong problem-solving and analytical thinking.
- Excellent communication and presentation skills.
- Ability to work in collaborative, cross-functional teams.
- Self-driven and proactive in exploring new technologies.
Good-to-Have Skills
- Exposure toNLP frameworks(Hugging Face, spaCy, NLTK).
- Computer Vision experience (OpenCV, YOLO, Detectron).
- Experience withbig data frameworks(Spark, Hadoop).
- Knowledge ofgenerative AI(LLMs, diffusion models, prompt engineering).
- Contribution to research papers, open-source projects, or Kaggle competitions.
- Familiarity withA/B testing and experimentation frameworks.
- Experience Requirements4 to 8 yearsof professional experience in AI/ML development.
- Proven track record of building and deploying ML models into production.
- Experience in solving business problems through applied machine learning.
KPIs / Success Metrics
- Accuracy, precision, recall, F1-score, or other relevant model performance metrics.
- Successful deployment of ML models with minimal downtime and robust monitoring.
- Reduction in data processing or inference time (efficiency improvements).
- Measurable business impact (e.g., improved predictions, reduced churn, better personalization).
- Contribution to team learning through code reviews, mentoring, and documentation.
- Staying updated and adopting relevant cutting-edge ML/AI techniques.
