{"id":3062272,"url":"https://alion.io/job/unisongroup-ai-solution-architect","title":"AI Solution Architect","company":{"id":448684,"name":"Unison Group","domain":"unisongroup.com","url":"https://alion.io/company/unisongroup","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":75,"open_postings":72,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-26T05:57:25Z"}},"role":"Solutions","role_family":"Solutions","seniority":"staff","employment_type":"contractor","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":29000,"max_usd":51000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":37},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Alteryx","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LLM","optional":false},{"name":"Milvus","optional":false},{"name":"MLFlow","optional":false},{"name":"NLP","optional":false},{"name":"OpenAI","optional":false},{"name":"Power BI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SAS","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"TensorFlow","optional":false},{"name":"Terraform","optional":false},{"name":"Agile","optional":true},{"name":"AWS","optional":true},{"name":"GCP","optional":true},{"name":"GDPR","optional":true},{"name":"Hallucination","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-02T00:00:00Z","employer_posted_date":"2026-10-02","last_verified_at":"2026-10-26T08:28:44Z","board_verified":true,"closed_at":null,"days_open":24,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":24},"description":"ROLE SUMMARY: We are looking for an AI Solution Architect who can take an enterprise AI initiative from vision to execution. You will shape AI strategy with C-level stakeholders, architect cloud-native GenAI and ML platforms on Azure and Databricks, and lead cross-functional teams across data science, MLOps and product engineering to deliver them. You are equally comfortable presenting a roadmap to a board, reviewing a RAG pipeline design and coaching an engineer through a production incident.\nExperience\n12+ years in data, analytics or software engineering, including 5+ years leading AI/ML platforms or products in an enterprise setting.\nProven record of taking ML and GenAI solutions into production at scale, with measurable business impact.\nHands-on delivery of at least one production LLM/RAG solution, plus hands-on experience with agentic AI patterns.\nExperience leading multidisciplinary teams and presenting to C-level executives.\nBachelor's or master's degree in computer science, Data Science, Engineering, Statistics or a related field.\nCloud & MLOps: Azure (ADF, Azure ML, Synapse, Event Hubs, Key Vault), Databricks, Kubernetes, Docker, Terraform, CI/CD\nData & ML: Python, SQL, PySpark, MLflow, TensorFlow, Gurobi, Dataiku, SAS, Alteryx\nGenAI & Agentic AI: OpenAI and other LLMs, LangChain, Milvus / vector databases, RAG, prompt engineering, memory agents, NLP, LLM evaluation\nBI & Visualisation: Power BI, Tableau, Spotfire, Qlik\nLeadership Competencies:\nAI strategy and roadmap execution\nAI/ML product lifecycle management\nResponsible AI and governance\nStakeholder engagement and executive communication\nTraining, change management and AI adoption\nCross-functional team leadership\nVendor and partner collaboration\nNice to Have\nCertifications such as Azure Solutions Architect Expert, Azure AI Engineer, Databricks ML Professional or TOGAF.\nExperience with AWS or GCP AI services, or multi-cloud architectures.\nIndustry exposure in financial services, energy, healthcare, manufacturing or the public sector.\nExperience designing or delivering AI training and enablement content.\nSUCCESS IN THE FIRST 12 MONTHS\nAn agreed AI roadmap and reference architecture adopted across key client engagements.\nAt least two GenAI or ML solutions in production with tracked business value.\nA working Responsible AI and MLOps framework reused by delivery teams.\nA high-performing, cross-functional team and a strong bench of client executive relationships.\nReports to: Chief Executive Officer / Head of AI\nRequirements\nAI Strategy & Leadership\nDefine and own AI strategy and multi-year roadmaps aligned to client and C-level business priorities, with clear value metrics (revenue, cost, risk, productivity).\nLead the full AI/ML product lifecycle: opportunity discovery, business case, architecture, build, deployment, adoption and value tracking.\nBuild, mentor and lead agile delivery teams spanning data scientists, ML/MLOps engineers, data engineers and product managers.\nAct as a trusted advisor to executives, translating complex technical options into clear decisions on investment, risk and trade-offs.\nSolution Architecture & Delivery\nArchitect enterprise-grade, cloud-native AI/ML and analytics platforms on Azure (ADF, Azure ML, Synapse, Event Hubs, Key Vault) and Databricks.\nDesign and deliver GenAI and agentic AI solutions: LLM integrations (OpenAI and others), retrieval-augmented generation (RAG), vector databases (e.g. Milvus), prompt engineering, memory-enabled agents and NLP pipelines.\nEstablish MLOps foundations using MLflow, Docker, Kubernetes and Terraform for reproducible training, CI/CD, monitoring and scalable model serving.\nSet architecture standards, reference designs and reusable components that reduce time-to-production across engagements.\nGuide data and analytics solutions end to end, from pipelines (Python, SQL, PySpark) to optimisation (Gurobi) and BI dashboards (Power BI, Tableau, Spotfire, Qlik).\nResponsible AI & Governance\nDefine and embed Responsible AI practices: fairness, explainability, privacy, security, model risk management and human oversight.\nDesign governance for GenAI, including evaluation frameworks, guardrails, hallucination and prompt-injection controls, cost monitoring and audit trails.\nEnsure solutions comply with relevant regulations and client policies (e.g. PDPA, GDPR, MAS FEAT principles where applicable).\nStakeholder, Change & Adoption\nEngage business, IT, security and risk stakeholders to align scope, secure buy-in and manage expectations.\nLead training, change management and AI adoption programmes so solutions are used, trusted and sustained.\nManage vendor and partner relationships (cloud providers, LLM providers, platform and SI partners), including evaluation, selection and commercial input.\nProduce clear executive communication: roadmaps, status reports, value realisation reviews and steering committee 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