KogniVera
Website:
kognivera.com
Job details:
Role Overview
We are seeking experienced AI/ML Developers to join our growing team. The ideal candidate will have hands-on experience delivering AI/ML projects end-to-end, a strong foundation in machine learning concepts, and a recognized AI/ML certification from a reputed institution. This role demands ownership of model development, deployment, and continuous improvement of AI/ML solutions in a collaborative engineering environment.
Experience Required
- Minimum 2–4 years of professional experience in AI/ML development.
- Demonstrable contribution to at least 3 AI/ML projects (production, client, or substantial proof-of-concept work). Candidates must be prepared to discuss architecture, dataset, modeling choices, evaluation, and outcomes for each project.
- Mandatory completion of an AI/ML certification from a recognized institution — for example, the IIT Delhi Generative AI & Automation Training Program (or an equivalent program from IIT/IISc/IIIT, Stanford, DeepLearning.AI, Google, AWS, or Microsoft). Certificate must be produced at the time of interview.
Key Responsibilities
- Design, develop, train, and deploy machine learning and deep learning models to solve real business problems.
- Own the full ML lifecycle: data ingestion, preprocessing, feature engineering, model training, evaluation, deployment, and monitoring.
- Build and maintain reusable ML pipelines and reproducible experimentation workflows.
- Collaborate with data engineers, product managers, and software engineers to integrate ML models into production systems via APIs and services.
- Optimize models for accuracy, latency, scalability, and cost.
- Conduct code reviews, write unit tests, and follow engineering best practices including version control and CI/CD.
- Stay current with developments in AI/ML and contribute to internal knowledge sharing.
Required Skills And Qualifications
- Bachelor's degree in AI/ML, Data Science, Computer Science, or a related quantitative discipline is MUST.
- Strong proficiency in Python programming, including object-oriented programming and writing production-grade code.
- Solid grasp of Machine Learning concepts: supervised/unsupervised learning, bias-variance trade-off, overfitting/underfitting, regularization, model evaluation, and hyperparameter tuning.
- Hands-on experience with at least two ML/DL libraries: scikit-learn, TensorFlow, Keras, or PyTorch.
- Practical experience with data preprocessing techniques: normalization, encoding, handling missing values, outlier detection, and feature engineering.
- Working knowledge of core algorithms: linear/logistic regression, decision trees, random forests, gradient boosting, clustering, and neural networks.
- Proficient in SQL and experience working with relational databases.
- Hands-on experience with Git and collaborative version-control workflows (branching, pull requests, code reviews).
- Experience deploying ML models to production using REST APIs (Flask/FastAPI) or similar frameworks.
- Ability to write clean, readable, modular, and well-documented code.
- Strong communication skills and the ability to collaborate effectively in cross-functional teams.
Project Portfolio (Mandatory)
Candidates must showcase a minimum of 3 AI/ML projects with the following details:
- Problem statement and business context.
- Dataset details, preprocessing steps, and feature engineering approach.
- Models evaluated, final model selected, and rationale.
- Evaluation metrics and quantitative results.
- Deployment approach (if applicable) and lessons learned.
- GitHub links, Kaggle notebooks, or production references are strongly preferred.
Certification Requirement (Mandatory)
Candidates must hold a completed AI/ML certification from a recognized institution. Acceptable examples include, but are not limited to:
- IIT Delhi — Generative AI & Automation Training Program (preferred).
- Equivalent AI/ML programs from IITs, IISc, IIITs, or other reputed Indian institutes.
- Globally recognized programs from Stanford Online, DeepLearning.AI, MIT, Google, AWS, or Microsoft.
- A copy of the certificate must be furnished at the time of the interview.
Nice-to-Have
- Hands-on experience with cloud platforms (AWS, GCP, or Azure) — especially with managed ML services such as SageMaker, Vertex AI, or Azure ML.
- Exposure to MLOps tools: MLflow, Airflow, Kubeflow, DVC, or Weights & Biases.
- Experience in specialized domains: NLP (transformers, LLMs, RAG), Computer Vision (CNNs, object detection), or Time Series Forecasting.
- Working knowledge of Generative AI, Large Language Models, prompt engineering, and frameworks like LangChain or LlamaIndex.
- Familiarity with Docker, Kubernetes, and CI/CD pipelines.
- Comfort with Jupyter Notebooks, Google Colab, and modern experiment-tracking tools.
- Participation in Kaggle competitions, AI hackathons, open-source contributions, or published research papers.
What We Offer
- Opportunity to work on impactful AI/ML projects across diverse business domains.
- A collaborative, learning-oriented engineering culture.
- Access to modern tools, cloud infrastructure, and continuous upskilling opportunities.
- Competitive compensation and growth path.
Panel - Phani
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