Medical Coding - Coding AI Assist (SME / Product Enablement)

2070Health · Bengaluru

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About Contiinex

Contiinex is a Bengaluru-headquartered technology company developing AI-powered solutions for the US healthcare Revenue Cycle Management (RCM) industry. Our solutions support and automate workflows across patient access, eligibility, prior authorization, medical coding, claims, denials, AR follow-up and collections. We work to convert real RCM operational expertise into practical, auditable software for healthcare providers and RCM service organizations.

The Opportunity: Build Coding AI Assist

We are hiring an experienced US Medical Coding Trainer to serve as the primary coding-domain expert for the design, development, training and validation of Contiinex Coding AI Assist.

This is not a routine classroom-training or production-coding position. You will convert real-world coding judgment into detailed specialty scenarios, coding rules, reference decisions, workflows and test cases that software engineers and AI teams can implement and evaluate.

The long-term product objective is to progress from coding assistance and human-reviewed recommendations toward safe, measurable autonomous coding for explicitly approved use cases. Coding decisions must remain grounded in complete documentation, applicable code-set versions, payer rules and appropriate human oversight. No programming or machine-learning experience is required.

Requirements

Key Responsibilities

Coding Knowledge to Engineering Requirements

• Own the coding-domain requirements for Coding AI Assist: describe input documents, clinical facts to extract, outputs, decision steps, exceptions, evidence, confidence or uncertainty, and when the system must defer to a human coder.
• Prepare step-by-step specialty coding workflows covering chart review, diagnosis selection and sequencing, procedure selection, E/M assignment, modifier application, medical necessity, claim edits and required documentation.
• Build a structured scenario library that includes routine, complex, ambiguous, incomplete, conflicting and rare cases; multiple diagnoses and procedures; post-operative care; global periods; add-on codes; exclusions; and coding changes over time.
• Write understandable rule specifications and decision trees for engineers, including trigger conditions, required documentation, exact expected coding decision, rationale, references, version and date applicability, exceptions and test acceptance criteria.
• Create de-identified or properly authorized example charts and synthetic scenarios with gold-standard coded answers; coordinate independent review and adjudicate disagreements with documented reasoning.
• Work directly with product managers, engineers, data and AI specialists and QA teams in requirement workshops, sprint reviews and defect triage. Explain clinical and coding concepts without assuming technical coding knowledge.
• Define which decisions are safe to automate, which require provider clarification or a compliant non-leading query, and which require specialist review rather than a guessed code.
• Maintain change-control documents as ICD-10-CM/PCS, CPT, HCPCS, official guidelines, NCCI, MUE and relevant payer and policy requirements change.

AI Training Data, Documents and Knowledge Artifacts

• Develop a medical coding taxonomy and specialty-by-specialty coverage matrix: encounters, services, procedures, diagnoses, terminology, document types, modifiers, billing contexts and common exception patterns.
• Author a coding rules repository, clinical-documentation checklist, scenario catalog, decision-tree library, annotated sample charts, reference coding dataset, query templates, error taxonomy and release and version history.
• For each case, capture source evidence, correct codes and sequencing, alternatives rejected and why, guideline or policy citation, effective date, expected AI output and escalation conditions.
• Build balanced training, validation and holdout test sets with varied note quality, clinical complexity, specialty, code frequencies and edge cases; prevent case leakage between evaluation sets.
• Define acceptance tests and measurable quality metrics: code-level accuracy, principal or first-listed diagnosis accuracy, E/M and modifier accuracy, unsupported-code rate, missed-code rate, escalation quality and specialty-specific performance.

AI Tool Testing, Evaluation and Continuous Improvement

• Run hands-on user acceptance testing (UAT) on new and updated Coding AI Assist builds; compare suggested codes and rationales against adjudicated reference answers.
• Identify overcoding, undercoding, unsupported specificity, missing codes, incorrect sequencing, modifier misuse, NCCI conflicts, E/M level errors, medical necessity issues and fabricated facts.
• Log reproducible defects with chart context, expected vs actual output, severity, guideline or reference, specialty, root-cause hypothesis and recommended correction.
• Retest fixes and regression suites across specialties and code-set versions; provide evidence-based go/no-go recommendations and monitor performance after release.
• Train internal teams and pilot clients on appropriate AI review, uncertainty, exception handling and safe use of the tool.

Specialties and Settings to Be Covered

The product roadmap aims for broad coverage across the following areas. One trainer is expected to own the overall scenario framework and prioritize coverage; expert validation or additional specialty SMEs will be used where depth or certification is needed.

• Primary care and ambulatory: family medicine, internal medicine, general practice, geriatrics, pediatrics, adolescent medicine, preventive medicine, occupational medicine, urgent care and telehealth.
• Medical subspecialties: cardiology, interventional cardiology, electrophysiology, pulmonology, sleep medicine, gastroenterology, hepatology, nephrology, endocrinology, rheumatology, allergy and immunology, infectious disease, hematology and oncology.
• Neurology and behavioral health: neurology, neurosurgery, psychiatry, psychology, behavioral health, addiction medicine, pain management and palliative and hospice care.
• Surgical specialties: general surgery, colorectal surgery, bariatric surgery, vascular surgery, cardiothoracic surgery, orthopedic surgery, spine surgery, hand surgery, plastic and reconstructive surgery, urology, otolaryngology (ENT), ophthalmology and podiatry.
• Women's and children's health: obstetrics, gynecology, maternal-fetal medicine, reproductive endocrinology and infertility, neonatology and neonatal and pediatric subspecialties.
• Diagnostic and procedural services: radiology, diagnostic and interventional imaging, nuclear medicine, radiation oncology, pathology, laboratory medicine, anesthesia, interventional pain, endoscopy and diagnostic testing.
• Other clinical services: dermatology, wound care, emergency medicine, critical care, intensive care, physical medicine and rehabilitation, physical, occupational and speech therapy, chiropractic, dental and oral-maxillofacial interfaces, and durable medical equipment, prosthetics, orthotics and supplies (DMEPOS).
• Care settings and coding streams: physician and professional-fee, outpatient clinic, hospital outpatient and observation, emergency department, ambulatory surgery center, inpatient facility (ICD-10-PCS/DRG), skilled nursing, home health and other settings based on product scope.

Coding Scenarios and Rules: Required Depth

• ICD-10-CM: diagnosis specificity, sequencing, combination codes, excludes notes, laterality, manifestations, complications, acute and chronic conditions, symptoms vs confirmed conditions, and uncertain diagnosis rules by setting.
• CPT/HCPCS: services, procedure components, add-on codes, code families, units, bilateral and unilateral services, global surgery concepts and place-of-service distinctions.
• E/M: selection using current MDM or time rules as applicable, new vs established patients, consultations where relevant, preventive visits, prolonged services, inpatient and outpatient, and critical care.
• Modifiers and edits: modifier selection (including 25, 59, XE, XP, XS, XU, 24, 57, 58, 78, 79, 26, TC and applicable anatomic and bilateral modifiers), NCCI PTP edits, MUEs and payer-specific edits.
• Medical necessity: LCD/NCD and other constraints, documentation insufficiency, provider queries, conflicting clinical statements and escalations.
• Updates and nuance: annual and periodic code-set or policy updates, specialty guideline differences, ambiguous cases, and differences between coding correctness and payer reimbursement rules.

Required Qualifications

• 5+ years of hands-on US medical coding experience, with substantial multi-specialty exposure. Prior experience as a coding trainer, coding auditor, QA lead or coding SME is strongly preferred.
• Active CPC (AAPC) or CCS/CCS-P (AHIMA) certification; additional specialty or auditing certifications are advantageous.
• Strong command of ICD-10-CM, CPT, HCPCS Level II, official coding guidelines, E/M, modifiers, NCCI edits, MUEs and medical necessity concepts.
• Ability to produce detailed, precise written SOPs, coding explanations, workflow diagrams, examples and test scenarios that non-coders can follow.
• Confident clinical-documentation interpretation, independent audit judgment, problem-solving and clear communication with engineering and product teams.
• Comfortable using spreadsheets, Word documents and structured issue trackers; willingness to work full-time from our Bengaluru office.

Preferred / Good to Have

• Experience creating training curricula, specialty coding playbooks, coding audit scorecards, clinical documentation improvement (CDI) guidelines or compliant provider queries.
• Exposure to computer-assisted coding (CAC), encoder products, coding audit applications or AI-enabled clinical documentation tools.
• Certifications such as CPMA, CRC, CIC, COC, CDIP, CCDS, specialty AAPC credentials or relevant AHIMA credentials.
• Experience with inpatient facility coding, ICD-10-PCS and DRG logic, or complex surgical, cardiology, oncology, radiology and anesthesia coding.

What Success Looks Like

• First 30 days: identify priority specialties, document source standards, agree coding decision format and create representative sample scenarios with the product team.
• By 60 to 90 days: deliver the first version of the specialty coverage matrix, rules library, annotated reference dataset, test plan and defect feedback process.
• Ongoing: expand reviewed scenario coverage, improve accuracy and escalation quality against independently adjudicated benchmarks, and support each product release with traceable testing evidence.

Benefits

Why Join Contiinex

Your coding expertise will directly shape how a new US healthcare coding AI product interprets documentation, makes recommendations and knows when to seek human review. You will collaborate with engineers from the earliest development phase and see your rules, scenarios and testing feedback translated into working product capabilities.

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