{"id":3462904,"url":"https://alion.io/job/techno-comp-data-scientist-artificial-intelligencemachine-learning","title":"Data Scientist - Artificial Intelligence/Machine Learning","company":{"id":3800577,"name":"Techno Comp","domain":"tccomputerservices.com","url":"https://alion.io/company/techno-comp","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India","Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":1000000,"max":2400000,"currency":"INR","period":"year","gross":true,"usd_annual":25332},"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"DVC","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Distillation","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Quantization","optional":false},{"name":"Sentiment Analysis","optional":false},{"name":"Service Mesh","optional":false},{"name":"A/B Testing","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"CloudFormation","optional":true},{"name":"FastAPI","optional":true},{"name":"GCP","optional":true},{"name":"Git","optional":true},{"name":"GitHub Actions","optional":true},{"name":"Helm","optional":true},{"name":"Hugging Face","optional":true},{"name":"Jenkins","optional":true},{"name":"LangChain","optional":true},{"name":"NumPy","optional":true},{"name":"Pandas","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"RAG","optional":true},{"name":"Recommender Systems","optional":true},{"name":"Rust","optional":true},{"name":"Scikit-learn","optional":true},{"name":"Semantic Search","optional":true},{"name":"Spark","optional":true},{"name":"SQL","optional":true},{"name":"TensorFlow","optional":true},{"name":"Terraform","optional":true},{"name":"TorchServe","optional":true},{"name":"Transformers","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-10-29T12:49:40Z","employer_posted_date":null,"last_verified_at":"2026-10-29T12:49:40Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Description :\n\nJOB RESPONSIBILITY :\n\n- Collaborate with cross-functional teams, including data scientists and product managers, to acquire, process, and manage data for AI/ML model integration and optimization.\n\n- Design and implement robust, scalable, and enterprise-grade data pipelines to support state-of-the-art AI/ML models.\n\n- Debug, optimize, and enhance machine learning models, ensuring quality assurance and performance improvements.\n\n- Operate container orchestration platforms like Kubernetes, with advanced configurations and service mesh implementations, for scalable ML workload deployments.\n\n- Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment.\n\n- Engage in advanced prompt engineering and fine-tuning of large language models (LLMs), focusing on semantic retrieval and chatbot development.\n\n- Document model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC.\n\n- Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs.\n\n- Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling.\n\n- Stay up-to-date with industry trends and advancements in AI technologies, integrating new methodologies and frameworks to continually enhance the AI engineering function.\n\n- Contribute to creating specialized AI solutions in healthcare, leveraging domain-specific knowledge for task adaptation and deployment.\n\nQUALIFICATION :\n\n- Minimum education: Bachelors degree in any Engineering Stream\n\n- Specialized training, certifications, and/or other special requirements: Nice to have\n\n- Preferred education: Computer Science/Engineering.\n\nEXPERIENCE : Minimum relevant experience - 4+ years in AI Engineering\n\nSKILLS AND COMPETENCIES :\n\nTechnical Skills :\n\n- Advanced proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow)\n\n- Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques\n\n- Experience with big data processing using Spark for large-scale data analytics\n\n- Version control and experiment tracking using Git and MLflow\n\n- Software Engineering & Development: Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing.\n\n- DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations.\n\n- LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management.\n\n- MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining.\n\n- Cloud & Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems.\n\n- LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security.\n\n- General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP.\n\n- Experience in creating LLD for the provided architecture.\n\n- Experience working in microservices based architecture.\n\nDomain Expertise :\n\n- Strong mathematical foundation in statistics, probability, linear algebra, and optimization\n\n- Deep understanding of ML and LLM development lifecycle, including fine-tuning and evaluation\n\n- Expertise in feature engineering, embedding optimization, and dimensionality reduction\n\n- Advanced knowledge of A/B testing, experimental design, and statistical hypothesis testing\n\n- Experience with RAG systems, vector databases, and semantic search implementation\n\n- Proficiency in LLM optimization techniques including quantization and knowledge distillation\n\n- Understanding of MLOps practices for model deployment and monitoring\n\nProfessional Competencies :\n\n- Strong analytical thinking with ability to solve complex ML challenges\n\n- Excellent communication skills for presenting technical findings to diverse audiences\n\n- Experience translating business requirements into data science solutions\n\n- Project management skills for coordinating ML experiments and deployments\n\n- Strong collaboration abilities for working with cross-functional teams\n\n- Dedication to staying current with latest ML research and best practices\n\n- Ability to mentor and share knowledge with team members\n\nSkills\nData Scientist, Data Science, Artificial Intelligence, Machine Learning, LLM, GPU, Python, Tensorflow, 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