Python - AI / ML Engineer : Noida

9 hours ago

Noida, Uttar Pradesh, India Software Engineering Firm Full time ₹ 9,00,000 - ₹ 12,00,000 per year
Python - AI / ML Engineer : Noida Location: Noida Experience: 4 to 8 Years
Role Summary & Key Objectives We are seeking an experienced AI / ML Engineer with 4–8 years of industry experience in designing, developing, and deploying scalable machine learning solutions. The role involves working across the full lifecycle of ML systems – from data acquisition and feature engineering to model training, optimization, deployment, and monitoring. The ideal candidate will collaborate with product, data, and engineering teams to solve real-world problems using advanced machine learning and AI techniques.
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 in Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).
  • Experience with machine learning algorithms (supervised, unsupervised, reinforcement learning).
  • Hands-on expertise in deep learning (CNNs, RNNs, Transformers).
  • Proficiency in data preprocessing, feature engineering, and statistical analysis.
  • Experience with SQL/NoSQL databases and handling large datasets.
  • Familiarity with MLOps 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 optimize foundation models for domain-specific applications
  • Agentic AI framework (AutoGen, LangChain/LangGraph, CrewAI, and Microsoft Semantic Kernel, OpenAI)
  • Experience in multimodal 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 to NLP frameworks (Hugging Face, spaCy, NLTK).
  • Computer Vision experience (OpenCV, YOLO, Detectron).
  • Experience with big data frameworks (Spark, Hadoop).
  • Knowledge of generative AI (LLMs, diffusion models, prompt engineering).
  • Contribution to research papers, open-source projects, or Kaggle competitions.
  • Familiarity with A/B testing and experimentation frameworks.

Experience Requirements
  • 4 to 8 years of 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.

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