{"id":1272507,"url":"https://alion.io/job/katalytx-analytics-ai-architect","title":"AI Architect - Pharma/Bioinformatics Domain","company":{"id":3806503,"name":"Katalytx Analytics","domain":"katalytx.com","url":"https://alion.io/company/katalytx-analytics","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":33000,"max_usd":76000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":13},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Embeddings","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-09-02T04:49:34Z","employer_posted_date":null,"last_verified_at":"2026-09-02T04:49:34Z","board_verified":false,"closed_at":null,"days_open":66,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":66},"description":"Job Description :\n\nWe are looking for a hands-on AI Architect with deep experience in Bioinformatics and Pharma domain applications, capable of designing and delivering production-grade AI systems. This role demands a strong blend of domain expertise, advanced machine learning engineering, and architectural leadership.\n\nYou will be responsible for driving AI initiatives end-to-end-from problem framing and model design to deployment and scaling in regulated environments. The ideal candidate brings practical experience in building AI/ML systems, combined with the ability to architect enterprise-grade solutions leveraging modern frameworks and Large Language Models (LLMs).\n\nThis is a high-impact role requiring close collaboration with business stakeholders, data scientists, and engineering teams to translate complex bioinformatics and pharma use cases into scalable, compliant AI solutions.\n\n10 - 15 yrs Exp |Pharma, Bio Informatics Domain | Immediate/Early joiners\n\nRole Requirements :\n\nExperience :\n\n- 10- 15 years of overall experience in software engineering / data / analytics.\n\n- Minimum 2- 3 years of hands-on AI/ML implementation in production environments.\n\nDomain Expertise (Mandatory) :\n\n- Proven experience in Bioinformatics, Life Sciences, or Pharma domain.\n\n- Exposure to datasets such as clinical, genomic, or molecular data is highly preferred.\n\nTechnical Skills :\n\n- Strong hands-on expertise in PyTorch, TensorFlow, scikit-learn\n\nExperience with :\n\n- Model training, tuning, and evaluation\n\n- Feature engineering and data pipelines\n\n- Deployment using APIs / microservices\n\nLLM & Modern AI Exposure :\n\n- Practical experience working with GPT, Claude, LLaMA\n\n- Understanding of RAG architectures, embeddings, vector databases, and prompt design\n\nLeadership & Execution :\n\n- Ability to own and deliver AI programs end-to-end\n\n- Strong communication skills with ability to engage senior stakeholders\n\nKey Responsibilities :\n\nAI Solution Architecture :\n\n- Design and implement scalable AI/ML architectures for bioinformatics and pharma use cases such as drug discovery, clinical data analysis, genomics, and real-world evidence.\n\n- Define end-to-end pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, and monitoring.\n\nEnd-to-End AI Delivery :\n\n- Lead AI initiatives from conceptualization to production deployment, ensuring robustness, scalability, and maintainability.\n\n- Build and deploy production-grade models using frameworks like TensorFlow, PyTorch, and scikit-learn.\n\nLLM & Advanced AI Integration :\n\n- Integrate LLMs (e.g., GPT, Claude, LLaMA) into enterprise workflows for use cases such as scientific literature mining, clinical insights extraction, and decision support.\n\n- Implement RAG pipelines, prompt engineering, and agent-based workflows where applicable.\n\nDomain-Driven AI Implementation :\n\n- Apply AI techniques to bioinformatics datasets (genomics, proteomics, clinical data).\n\n- Ensure solutions align with pharma domain constraints, including data sensitivity and regulatory requirements.\n\nTechnical Leadership :\n\n- Provide architectural guidance and hands-on mentoring to data scientists and ML engineers.\n\n- Establish best practices in MLOps, model versioning, CI/CD, and performance optimization.\n\nStakeholder Engagement : \n\n- Collaborate with CXOs, domain experts, and business leaders to align AI initiatives with strategic objectives.\n\n- Translate business problems into technical solution blueprints.\nSkills\nArtificial Intelligence, Machine Learning, Bioinformatics, LLM, GPT, AI Integration, Prompt Engineering","description_format":"text","description_chars":3610,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Bioinformatics","Management Consulting","Analytics & BI Consulting"],"lifecycle":[{"event":"open","at":"2026-09-26T00:07:01Z"}],"visa":[],"liveness":{"score":6,"band":"cold","label":"Long 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