{"id":2001571,"url":"https://alion.io/job/lingaro-aiml-engineer-freelancer","title":"AI/ML Engineer - freelancer","company":{"id":53383,"name":"Lingaro","domain":"lingarogroup.com","url":"https://alion.io/company/lingaro","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":88,"open_postings":29,"ghost_share":0,"stale_share":0.483,"repost_share":0,"time_to_fill_p50_days":41,"computed_at":"2026-10-12T06:55:09Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"freelance","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Poland"],"countries":["PL"],"hiring_countries":["PL"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":63000,"max_usd":110000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":44},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"Agile","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Tool Use","optional":false}],"status":"live","first_seen_at":"2026-10-07T09:32:37Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-12T12:59:39Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"Tasks:\nBuild and continuously evolve shared AI capabilities, reusable services, and engineering accelerators that can be adopted across multiple construction software products.\nDesign and implement production-grade AI systems including LLM-based experiences, retrieval pipelines, tool use, orchestration layers, and agentic workflows.\nCreate human- and machine-executable technical artifacts such as specs, evals, interface definitions, and workflow contracts that improve clarity, speed, and quality of delivery.\nDefine and apply evaluation-driven development loops for AI systems, covering quality, reliability, latency, cost, grounding, and other relevant performance dimensions.\nPartner with product teams to identify high-value cross-product opportunities, support integration of shared capabilities, and recommend patterns without owning product-specific architecture decisions.\nContribute to AI-native engineering practices inside the team through Spec-Driven Development, AI-assisted development workflows, automated testing, and intelligent engineering agents.\nApply practical standards and guardrails for AI reliability, observability, governance, security, and responsible production use of AI solutions delivered by the central team.\nPrototype and validate new technical approaches quickly, turning emerging AI capabilities into scalable building blocks with clear business relevance.\nSupport and guide other engineers through technical collaboration, peer review, and knowledge sharing in modern AI engineering and experimentation.\nWork closely with AI PM / PO, engineering, and business stakeholders to align implementation choices with customer value, delivery feasibility, and measurable outcomes.\nWhat We're Looking For:\nAt least 5+ years of hands-on software engineering experience, including significant work on production-grade AI or data-intensive systems in complex delivery environments.\nExcellent Python programming skills.\nProficiency in Azure /AWS cloud platforms.\nHands-on experience with Generative AI technologies and applications. \nStrong practical understanding of LLMs, retrieval-augmented generation, tool use, agents, and their real-world trade-offs, limitations, and failure modes.\nExperience designing and operating end-to-end AI systems across APIs, application services, data pipelines, retrieval layers, evaluation workflows, and cloud infrastructure.\nAbility to define measurable quality criteria and evaluation approaches for AI systems, balancing customer value with reliability, latency, cost, and maintainability.\nPractical experience with AI-assisted engineering, Spec-Driven Development, and modern developer tooling; able to improve how teams work by combining human judgment with automation and agent support.\nStrong architectural judgment with the ability to design reusable patterns and shared capabilities while collaborating effectively with product teams that own their local implementation decisions.\nDemonstrated track record of contributing to cross-functional work from early exploration and prototyping through production rollout and continuous improvement in agile environments.\nAbility to support and influence other engineers, raise technical standards through collaboration, and create clarity in ambiguous and fast-changing AI environments.\nStrong communication and collaboration skills, with the ability to translate technical complexity into decisions and actions for product, engineering, and business stakeholders.\nAdaptive mindset, high learning agility, and genuine openness to continuous change in a field shaped by rapid advances in AI.\nMaster's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related technical field; equivalent experience considered for exceptional candidates.\n\nMissing one or two of these qualifications? We still want to hear from you! 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