• Design, implement, and maintain Python-based services and workflows that integrate LLMs and GenAI capabilities with internal platforms and applications. ** • Build and iterate on agentic and multi-step workflows using approved orchestration frameworks and platform patterns (e.g., LangGraph, AgentCore, LangChain). ** • Consume existing retrieval/RAG and search abstractions to improve response quality, grounding, and reliability, tuning parameters (top-k, scoring, filters) rather than re-implementing core retrieval infrastructure. ** • Develop robust tooling and APIs for agents, including clear input/output schemas, error contracts, versioning, and observability hooks. ** • Implement structured output handling (e.g., JSON schemas, tool calls) to ensure predictable behavior and easier downstream integration. • Operate within established platform, security, and governance guardrails (RBAC, data access boundaries, PII handling, logging, audit) instead of building custom, one-off mechanisms. ** • Leverage existing platform SDKs, templates, and patterns for configuration, deployment, and monitoring of GenAI workloads. ** • Work in a cloud-native AWS and Azure environments (e.g., cloud native applications, environment variables, secrets management, logging/metrics/tracing), collaborating with platform teams as needed rather than owning core infra design. • Partner with product managers, internal business stakeholders, and UX to translate problem statements and evaluation criteria into concrete, production-ready workflows. ** • Collaborate with data and application teams to integrate GenAI capabilities into existing systems (e.g., internal tools, portals, automation flows) with minimal disruption. ** • Participate in design reviews, code reviews, and architecture discussions, ensuring solutions are maintainable, observable, and aligned with platform standards. ** • Contribute to internal enablement (playbooks, examples, patterns) to help eTech Engineering and other teams become effective consumers of GenAI capabilities. • Own the operational health of GenAI workflows you build: monitoring, alerting, troubleshooting, and iterative improvement. ** • Incorporate evaluation and guardrail checks (e.g., automated tests, evaluation harnesses, red/blue team feedback) into workflows to improve quality and safety over time. ** • Act as a “high adopter of AI to build AI”, continuously using AI tools (e.g., Glean, Devin, Windsurf, Claude) to accelerate design, development, testing, and documentation.

• Design the agentic systems that run across hospitality design, hospitality strategy, and marketing workflows • Decide the stack: which tools, which agents, which integrations, what memory architecture, what evaluation harness • Document the architecture so it compounds rather than evaporates when you take leave • Build agents using Claude, ChatGPT, Make, n8n, Zapier, the Claude Agent SDK, and adjacent platforms • Maintain integrations across our stack: Gmail, Google Workspace, Streak, Xero, WhatsApp, ArchiCAD outputs, WordPress, Meta Ads, Google Ads, LinkedIn, Klaviyo • Build evaluation and observability into every agent. We need to know when one fails. • Build AI-assisted content systems for LinkedIn, Instagram, blog, EDM and short-form video, calibrated to brand voice per client • Architect the brand voice infrastructure: founder interviews to voice guide to enforcement layer across all content output • Build performance reporting pipelines that pull Meta Ads, Google Ads, LinkedIn Ads, and organic data into client-ready insights • Design ad creative generation and variation pipelines tied to performance feedback loops • Build SEO and AI-search optimisation systems (GEO / AEO), including keyword clustering, content briefs, on-page optimisation, and schema markup • Build lead intake systems across website, WhatsApp, referral and inbound email, with classification and routing • Design qualification scoring against our ICP, and the agent layer that drafts initial follow-ups • Architect the CRM rhythm in Streak: deal stage updates from meeting transcripts, proposal follow-up cadence, pipeline reporting • Build fee proposal generation: brief in, branded proposal out, with version control and follow-up logic • Design LinkedIn lead-scraping, enrichment, and personalised outreach workflows for BDM • Build the trigger logic for which deals sees personally and which the system handles • Build the project workspace generation that triggers when a deal is won: folder structure, templates, access, programme, kickoff brief • Design the reverse brief drafting agent that converts kickoff notes into structured project briefs • Architect the cost planning workflow: from sketch to v0 cost plan in our FOH/BOH/Services/FFE/Prelims structure, with builder benchmark lookups • Build the builder budget revision loop: receive revised budget, reconcile against cost plan, draft client memo, schedule meeting • Design RFI log management, drawing register hygiene, and subcontractor follow-up systems • Build the weekly client status reporting layer per active project • Support bookkeeping rhythm with automation: Xero reconciliation triggers, invoice issuance against milestones, debtor follow-up cadence, BAS prep • Embed AI into the daily operating rhythm of three businesses • Coach the team on how to use the systems you build. We're not buying tools, we're buying capability. • Stay across emerging AI tools, agents, frameworks and platform changes • Bring proactive ideas. Test them. Tell us what worked and what didn't. • Identify where AI extends judgement vs replaces it. Defend the line. • AI is integrated into daily workflows across all three businesses in a measurable, defensible way • The founder recovers significant weekly hours through agent-mediated work • Project status reporting, cost plan generation, and client comms run on assisted workflows we can prove the ROI on • Bookkeeping rhythm (reconciliation, invoicing, debtor chase) is supported by automation • No Filter scales content production without proportional headcount growth • We have a documented, defensible AI operating layer that the business owns, not the vendors.

Title: AI Engineer Location: Dallas-Fort Worth Metroplex, TX Job Description: Location: Remote (U.S.-based candidates only). Preference for candidates based in Dallas, TX. 2-3 days on-site. Employment Type: Contractor Compensation: $65-$80/hr. Compensation will be determined based on relevant experience, skills, and overall qualifications. Requirements: Must be authorized to work legally in the US without sponsorship, now or in the future. About Us Concord isn't your typical consulting firm; we are an execution company with a passion for making things happen. Our mission is to help clients enhance customer experiences, optimize operations, and revolutionize their product offerings through seamless integration, optimization, and activation of technology and data. We are purpose-built, merging the industry’s top specialty companies to amplify our Innovation Capabilities in analytics & AI, data management & engineering, UX and digital experience, and technical platform integration, automation & security engineering. About the Role We are seeking a skilled AI Engineer to join a client engagement focused on building and deploying intelligent, production-ready AI solutions. This is a hands-on role for someone who thrives at the intersection of large language models, applied ML, and real-world system design. You will work closely with cross-functional teams to implement AI-driven features that deliver measurable business impact. What You’ll Be Doing - Design, develop, and optimize LLM-powered applications using prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) architectures. - Build and maintain agentic workflows and multi-step AI pipelines using frameworks such as LangChain, LlamaIndex, or AutoGen. - Evaluate, select, and integrate appropriate LLMs (OpenAI, Anthropic, Mistral, Llama, Gemini, etc.) based on use case requirements. - Collaborate with data, product, and engineering teams to move AI solutions from prototype to production. - Continuously monitor model performance, identify failure modes, and implement improvements Document architecture decisions and maintain clean, scalable Python codebases. Experience and Skills Required Experience: - 4–7 years of software engineering experience, with at least 2 years focused on AI/ML or LLM development. - Strong Python proficiency. - Hands-on experience with prompt engineering techniques (chain-of-thought, few-shot, system prompting, etc.). - Experience fine-tuning LLMs (LoRA, PEFT, instruction tuning, or similar approaches). - Solid understanding of RAG architecture — chunking strategies, embedding models, retrieval optimization. - Experience designing and implementing agent workflows (tool use, memory, planning loops). - Familiarity with multiple LLM providers and the ability to evaluate trade-offs across models Preferred Experience: - Experience with vector databases (Pinecone, Weaviate, Chroma, pgvector). - Cloud platform experience — AWS, Azure, or GCP. - MLOps exposure: model versioning, deployment pipelines, monitoring (MLflow, Weights & Biases, etc.). - Background in the retail industry — personalization engines, recommendation systems, inventory/demand forecasting, or customer-facing AI features is a strong plus. - Familiarity with LLM evaluation frameworks (Ragas, DeepEval, etc.). - Experience with multimodal models or vision-language applications. Soft Skills & Delivery: - Technically fluent but able to communicate clearly in business terms — comfortable presenting architecture decisions to both engineering teams and executive stakeholders. - Strategic thinker who operates well in high-stakes environments and can drive clarity. - Collaborative leader who thrives in building alignment across cross-functional teams. What We Offer (only for W-2 Employment) - Health, Dental, and Vision Insurance: Comprehensive coverage to support your overall well-being. - Employer Contributions to Health Savings Accounts (HSA): For employees enrolled in High Deductible Health Plans, Concord contributes toward your HSA. - Flexible Spending Accounts (FSA): Options for healthcare and dependent care expenses, plus a Lifestyle Spending Account (LSA) for wellness and personal development. - Disability Insurance: Short-term (up to 12 weeks) and long-term coverage, fully paid by the employer. - Life and AD&D Insurance: Employer-provided coverage with options for additional voluntary coverage. - Employee Assistance Program (EAP): Support for legal, financial, and personal challenges. - 401(k) Retirement Savings: 1% employer match. - Career Growth Opportunities: Pathways for advancement and skill development. - Team Engagement Activities: Regular team-building events and company-sponsored activities to foster collaboration and connection. Concord is an execution partner helping organizations drive digital transformation, modernization, and scalable technology solutions. We deliver results that solve real business challenges. We operate globally and are growing fast, shaping the future of technology. Join a team trusted by top companies to drive strategic growth and operational excellence!

Title: AI Engineer Location: Remote Job Description: Anika Systems is an outcome-driven technology solutions firm that guides federal agencies in solving complex business challenges and preparing for the future. Our services span AI Strategy, Data Intelligence, AI & Machine Learning, Intelligent Automation, Enterprise Platforms and Engineering, with a specialized focus on National Security and Federal Financial programs. We are dedicated to delivering forward-thinking solutions that accelerate the critical missions of our government clients. Through our VITAL Innovation Lab, we invest heavily in research and development to anticipate mission needs and deliver cutting edge solutions. This position is currently remote; however, in accordance with federal contract requirements and company policy, there may be future changes to work location expectations. Candidates should be prepared for the possibility of a return to the office, either partially or fully, based on client directives, contractual obligations, or company policies. Any such changes will be communicated in advance. Must be a U.S. Citizen with the ability to obtain and maintain a government suitability clearance. The AI Engineers will design and deliver production AI systems across our commercial platform work and our federal modernization programs. This is a hands-on builder role on a small, senior team. Engineers own work end-to-end including data, model integration, services, UI, infrastructure, security, and deployment. You will be expected to make sound technical calls under federal compliance, security, and procurement constraints. Requirements: - Education: Bachelor’s Degree in Computer Science, Computer Engineering, Data Science, Computational Linguistics, or a related field; Master’s is a bonus. - Experience: 1-3 years of professional experience building and shipping ML or NLP systems, including focus on LLM based or Generative AI solutions. - Strong production experience in Python and TypeScript, with modern web frameworks on both sides (e.g. FastAPI, React). - Hands-on integration of LLMs into real applications — streaming, tool / function calling, token accounting, and failure handling — across at least one of OpenAI, Anthropic, Bedrock, Vertex, or Azure OpenAI. - Experience designing agentic systems: multi-step workflows, retrieval-augmented generation, evaluation, and human-in-the-loop guardrails. - Solid relational data modeling on PostgreSQL with an async ORM and migration tooling. - AWS fluency (compute, storage, IAM, secrets, networking, observability) and infrastructure-as-code, ideallyTerraform. - Practical understanding of OAuth / OIDC, JWT, RBAC, multi-tenant isolation, and least-privilege design. - Disciplined testing, linting, and type-checking habits; reviewable diffs and clear written communication. Preferred Skills - Federal program exposure: NIST 800-53, FedRAMP, FISMA, ATO process, or agency-specific data-handling requirements. - Experience with Keycloak or another OIDC provider, RLS-based multi-tenancy, or zero-trust application design. - Working knowledge of a data/lakehouse platform (Databricks, Snowflake) and SQL beyond CRUD. - Document AI / OCR pipelines and modernization of legacy enterprise workflows. - Production observability (CloudWatch, OpenTelemetry, Grafana) and incident-response experience. - Supply-chain hardening: SBOMs, dependency scanning, signed builds, threat modeling.