Key Objectives:

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:

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.

Lead AI/ML Engineer

AI/ML Engineer