AI/ML Engineer
IVTEX Corporate Solutions Private Limited · Bhubaneswar, Odisha, India
Apply & track with Apply EdgeCompany Description Ivtex Corporate Solutions Private Limited delivers high-quality, technology-driven solutions tailored to clients’ evolving business needs. The organization specializes in AI, Data Analytics, Consulting, Emerging Technologies, and Cybersecurity, helping companies stay competitive in an increasingly digital world. Ivtex collaborates with organizations across diverse industries to design robust, scalable, and future-ready solutions. Its focus on efficiency, innovation, and measurable outcomes creates a dynamic environment for professionals interested in advanced technology and applied AI.Job Description – AI/ML EngineerPosition: AI/ML Engineer
The role may also involve Generative AI, Large Language Models (LLMs), computer vision, NLP, and intelligent automation depending on project requirements.Key ResponsibilitiesDesign, develop, train, test, and deploy Machine Learning and Deep Learning models.Analyze and preprocess structured and unstructured datasets for model development.Perform data cleaning, feature engineering, feature selection, and exploratory data analysis.Develop predictive models, classification models, recommendation systems, forecasting solutions, and other ML-based applications.Build solutions using Python and ML frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or equivalent technologies.Develop and integrate Generative AI and LLM-based applications, where required.Work with LLM APIs and open-source models for applications such as chatbots, document intelligence, summarization, information extraction, and knowledge assistants.Implement RAG (Retrieval-Augmented Generation) pipelines, embeddings, vector search, and prompt engineering for GenAI applications.Work on NLP and/or Computer Vision solutions based on project requirements.Develop REST APIs using frameworks such as FastAPI or Flask for AI/ML model integration.Deploy ML models into production environments and integrate them with web/mobile/backend applications.Optimize models for accuracy, latency, scalability, and computational efficiency.Establish model evaluation metrics and conduct validation, testing, and performance benchmarking.Monitor deployed models for performance degradation, data drift, model drift, and other production issues.Maintain proper model versioning, experiment tracking, documentation, and reproducibility.Collaborate with software developers, data engineers, business analysts, QA teams, and project managers.Convert business requirements into technically feasible AI/ML solutions.Research emerging AI/ML technologies and evaluate their applicability to organizational projects.Follow data privacy, information security, and responsible AI practices.Required Technical SkillsProgramming:Python, SQLMachine Learning:Scikit-learn, Pandas, NumPy, XGBoost/LightGBM, feature engineering, supervised and unsupervised learning, model evaluation and optimizationDeep Learning:PyTorch and/or TensorFlow/Keras, neural networks, CNNs, TransformersGenerative AI / LLM:LLMs, prompt engineering, embeddings, RAG, vector databases, LLM APIs and/or open-source modelsNLP:Text preprocessing, classification, information extraction, semantic search, transformersComputer Vision – Preferred:OpenCV, object detection, image classification, OCR and related vision modelsAPI Development:FastAPI / Flask / REST API integrationDatabases:MySQL/PostgreSQL and exposure to NoSQL databasesMLOps / Deployment:Docker, Git, CI/CD fundamentals, model versioning, experiment tracking and production model monitoringCloud – Preferred:AWS / Microsoft Azure / Google Cloud PlatformPreferred Tools & TechnologiesExposure to some of the following will be advantageous:Hugging Face TransformersLangChain / LlamaIndex or similar frameworksFAISS / Pinecone / Weaviate / Chroma or other vector databasesMLflow / Weights & BiasesJupyter NotebookGit/GitHub/GitLabDockerKubernetesApache AirflowOpenCVONNXCloud-based AI/ML servicesRequired KnowledgeThe candidate should have a strong understanding of:Machine Learning algorithmsStatistics and probabilityLinear algebra fundamentalsData preprocessing and feature engineeringModel selection and hyperparameter tuningClassification, regression and clusteringDeep Learning architecturesTransformers and modern AI architecturesModel evaluation metricsData structures and algorithmsML deployment and inference pipelinesAPI-based model integrationData security and privacy principlesKey CompetenciesStrong analytical and problem-solving abilityAbility to independently troubleshoot technical issuesStrong coding and debugging skillsResearch-oriented approach to emerging AI technologiesAbility to understand business problems and translate them into AI solutionsGood documentation skillsEffective communication and cross-functional collaborationAbility to manage multiple development tasks and project deadlinesPreferred Candidate ProfilePreference will be given to candidates who have:Developed and deployed at least one production-level AI/ML application.Hands-on experience taking an ML project from data preparation → model development → deployment → monitoring.Experience developing GenAI/LLM applications using RAG or similar architectures.Experience integrating AI models with existing software applications.Experience working with large datasets and production databases.A strong GitHub portfolio or demonstrable AI/ML projects.Experience working on government, enterprise, smart-city, surveillance, analytics, or large-scale technology projects will be an added advantage.Selection ProcessCandidates may be evaluated through:Resume/Profile ScreeningTechnical InterviewPractical AI/ML Assignment or Coding TestProject/Model DemonstrationFinal Technical/Management DiscussionDuring the technical evaluation, candidates should be prepared to explain one AI/ML project end-to-end, including the business problem, dataset, preprocessing, model selection, evaluation metrics, deployment architecture, challenges faced, and final results.Expected DeliverablesThe selected AI/ML Engineer will be expected to deliver reliable and production-ready AI solutions, maintain appropriate technical documentation, support deployment and integration, monitor model performance, and continuously improve AI/ML systems based on business and project requirements.